.
Step 1: Preparation¶
# Getting data
!pip install git+https://gitlab.aicrowd.com/yoogottamk/aicrowd-cli.git
from aicrowd.magic import *
!aicrowd login --api-key <key>
!aicrowd dataset download --jobs 5 --challenge imgcol *v2.zip
# Moving things into the right locations
!rm -rf data
!mkdir data
!mv 'test_black_white_images-v2.zip ' test_black_white_images-v2.zip
!mv 'validation_black_white_images-v2.zip ' validation_black_white_images-v2.zip
!unzip train_black_white_images-v2.zip -d data/
!unzip train_color_images-v2.zip -d data/
!unzip validation_black_white_images-v2.zip -d data/
!unzip validation_color_images-v2.zip -d data/
!unzip test_black_white_images-v2.zip -d data/
Import modules¶
import numpy as np
import pandas as pd
from glob import glob
from os.path import join
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm
from skimage.color import *
import tensorflow as tf
Data Preparation¶
# Important parameters
img_size = 512
# Get filepath to all images
fimgs_tr_x = sorted(glob('data/train_black_white_images-v2/*'))
fimgs_tr_y = sorted(glob('data/train_color_images-v2/*'))
fimgs_va_x = sorted(glob('data/validation_black_white_images-v2/*'))
fimgs_va_y = sorted(glob('data/validation_color_images-v2/*'))
fimgs_te_x = sorted(glob('data/test_black_white_images-v2/*'))
# Combine train and validation set
fx_train = fimgs_tr_x + fimgs_va_x
fy_train = fimgs_tr_y + fimgs_va_y
Note!!!¶
Most of the following code is originally coming from this medium post. I've adapted them slighty and changed some of the NN architecture but credit to it goes clearly to the original auhtor.
2. The GAN¶
In this section, we'll create our GAN model step-by-step with Keras. First, we'll implement the generator then the discriminator and finally the loss functions required by both of them.
A. Generator¶
Our generator ( represented as $G$ ) will take in grayscale image $x$ and produce a LAB image $G( x )$. Note, $x$ will be a tensor of shape $( \ batch \ size \ , \ 512 \ , \ 512 \ , \ 1 \ )$ and the output $G(x)$ will have a shape $( \ batch \ size \ , \ 512 \ , \ 512 \ , \ 3 \ )$
Our generator will have a encoder-decoder structure, similar to the UNet architecture. Additionally, we use Dilated convolutions to have a larger receptive field.
We introduce skip connections in our model so as to have better flow of information from the encoder to the decoder.
from tensorflow.keras.layers import (
BatchNormalization, Conv2D, Conv2DTranspose, Dense,
Flatten, Dropout, UpSampling2D, Concatenate, ELU,
Input, LeakyReLU, MaxPooling2D, Reshape, UpSampling2D)
def get_generator_model(ndim=32):
#activation = LeakyReLU
activation = ELU
inputs = Input(shape=(img_size, img_size, 1))
conv1 = Conv2D(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(inputs)
conv1 = BatchNormalization()(conv1)
conv1 = activation()(conv1)
conv1 = Conv2D(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv1)
conv1 = BatchNormalization()(conv1)
conv1 = activation()(conv1)
pool1 = MaxPooling2D()(conv1)
conv2 = Conv2D(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool1)
conv2 = BatchNormalization()(conv2)
conv2 = activation()(conv2)
conv2 = Conv2D(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
conv2 = BatchNormalization()(conv2)
conv2 = activation()(conv2)
pool2 = MaxPooling2D()(conv2)
conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool2)
conv3 = BatchNormalization()(conv3)
conv3 = activation()(conv3)
conv3 = MaxPooling2D()(conv3)
conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
conv3 = BatchNormalization()(conv3)
conv3 = activation()(conv3)
pool3 = MaxPooling2D()(conv3)
conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool3)
conv4 = BatchNormalization()(conv4)
conv4 = activation()(conv4)
conv4 = MaxPooling2D()(conv4)
conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
conv4 = BatchNormalization()(conv4)
conv4 = activation()(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D()(drop4)
conv5 = Conv2D(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool4)
conv5 = BatchNormalization()(conv5)
conv5 = activation()(conv5)
conv5 = MaxPooling2D()(conv5)
conv5 = Conv2D(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv5)
conv5 = BatchNormalization()(conv5)
conv5 = activation()(conv5)
drop5 = Dropout(0.5)(conv5)
bottleneck = Flatten()(drop5)
bottleneck = Dense(1024)(bottleneck)
bottleneck = Dense(ndim*256)(bottleneck)
bottleneck = Reshape((4, 4, 16*ndim))(bottleneck)
conv_up_1 = Conv2DTranspose(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(bottleneck)
merge_1 = Concatenate()([conv5, conv_up_1])
conv_up_1 = BatchNormalization()(merge_1)
conv_up_1 = activation()(conv_up_1)
conv_up_1 = UpSampling2D()(conv_up_1)
conv_up_1 = Conv2DTranspose(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_1)
conv_up_1 = BatchNormalization()(conv_up_1)
conv_up_1 = activation()(conv_up_1)
conv_up_1 = UpSampling2D()(conv_up_1)
conv_up_2 = Conv2DTranspose(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_1)
merge_2 = Concatenate()([conv4, conv_up_2])
conv_up_2 = BatchNormalization()(merge_2)
conv_up_2 = activation()(conv_up_2)
conv_up_2 = UpSampling2D()(conv_up_2)
conv_up_2 = Conv2DTranspose(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_2)
conv_up_2 = BatchNormalization()(conv_up_2)
conv_up_2 = activation()(conv_up_2)
conv_up_2 = UpSampling2D()(conv_up_2)
conv_up_3 = Conv2DTranspose(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_2)
merge_3 = Concatenate()([conv3, conv_up_3])
conv_up_3 = BatchNormalization()(merge_3)
conv_up_3 = activation()(conv_up_3)
conv_up_3 = UpSampling2D()(conv_up_3)
conv_up_3 = Conv2DTranspose(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_3)
conv_up_3 = BatchNormalization()(conv_up_3)
conv_up_3 = activation()(conv_up_3)
conv_up_3 = UpSampling2D()(conv_up_3)
conv_up_4 = Conv2DTranspose(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_3)
merge_4 = Concatenate()([conv2, conv_up_4])
conv_up_4 = BatchNormalization()(merge_4)
conv_up_4 = activation()(conv_up_4)
conv_up_4 = Conv2DTranspose(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_4)
conv_up_4 = BatchNormalization()(conv_up_4)
conv_up_4 = activation()(conv_up_4)
conv_up_4 = UpSampling2D()(conv_up_4)
conv_up_5 = Conv2DTranspose(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_4)
merge_5 = Concatenate()([conv1, conv_up_5])
conv_up_5 = BatchNormalization()(merge_5)
conv_up_5 = activation()(conv_up_5)
conv_up_5 = Conv2DTranspose(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_5)
conv_up_5 = BatchNormalization()(conv_up_5)
conv_up_5 = activation()(conv_up_5)
outputs = Conv2DTranspose(3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_5)
model = tf.keras.models.Model(inputs, outputs)
return model
def get_generator_model(ndim=32):
#activation = LeakyReLU
activation = ELU
inputs = Input(shape=(img_size, img_size, 1))
conv1 = Conv2D(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(inputs)
conv1 = BatchNormalization()(conv1)
conv1 = activation()(conv1)
conv1 = MaxPooling2D()(conv1)
conv1 = Conv2D(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv1)
conv1 = BatchNormalization()(conv1)
conv1 = activation()(conv1)
pool1 = MaxPooling2D()(conv1)
conv2 = Conv2D(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool1)
conv2 = BatchNormalization()(conv2)
conv2 = activation()(conv2)
conv2 = MaxPooling2D()(conv2)
conv2 = Conv2D(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
conv2 = BatchNormalization()(conv2)
conv2 = activation()(conv2)
pool2 = MaxPooling2D()(conv2)
conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool2)
conv3 = BatchNormalization()(conv3)
conv3 = activation()(conv3)
conv3 = MaxPooling2D()(conv3)
conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
conv3 = BatchNormalization()(conv3)
conv3 = activation()(conv3)
pool3 = MaxPooling2D()(conv3)
conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool3)
conv4 = BatchNormalization()(conv4)
conv4 = activation()(conv4)
conv4 = MaxPooling2D()(conv4)
conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
conv4 = BatchNormalization()(conv4)
conv4 = activation()(conv4)
drop4 = Dropout(0.5)(conv4)
bottleneck = Flatten()(drop4)
bottleneck = Dense(1024)(bottleneck)
bottleneck = Dense(ndim*128)(bottleneck)
bottleneck = Reshape((4, 4, 8*ndim))(bottleneck)
conv_up_1 = Conv2DTranspose(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(bottleneck)
merge_1 = Concatenate()([conv4, conv_up_1])
conv_up_1 = BatchNormalization()(merge_1)
conv_up_1 = activation()(conv_up_1)
conv_up_1 = UpSampling2D()(conv_up_1)
conv_up_1 = Conv2DTranspose(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_1)
conv_up_1 = BatchNormalization()(conv_up_1)
conv_up_1 = activation()(conv_up_1)
conv_up_1 = UpSampling2D()(conv_up_1)
conv_up_2 = Conv2DTranspose(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_1)
merge_2 = Concatenate()([conv3, conv_up_2])
conv_up_2 = BatchNormalization()(merge_2)
conv_up_2 = activation()(conv_up_2)
conv_up_2 = UpSampling2D()(conv_up_2)
conv_up_2 = Conv2DTranspose(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_2)
conv_up_2 = BatchNormalization()(conv_up_2)
conv_up_2 = activation()(conv_up_2)
conv_up_2 = UpSampling2D()(conv_up_2)
conv_up_3 = Conv2DTranspose(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_2)
merge_3 = Concatenate()([conv2, conv_up_3])
conv_up_3 = BatchNormalization()(merge_3)
conv_up_3 = activation()(conv_up_3)
conv_up_3 = UpSampling2D()(conv_up_3)
conv_up_3 = Conv2DTranspose(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_3)
conv_up_3 = BatchNormalization()(conv_up_3)
conv_up_3 = activation()(conv_up_3)
conv_up_3 = UpSampling2D()(conv_up_3)
conv_up_4 = Conv2DTranspose(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_3)
merge_4 = Concatenate()([conv1, conv_up_4])
conv_up_4 = BatchNormalization()(merge_4)
conv_up_4 = activation()(conv_up_4)
conv_up_4 = UpSampling2D()(conv_up_4)
conv_up_4 = Conv2DTranspose(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_4)
conv_up_4 = BatchNormalization()(conv_up_4)
conv_up_4 = activation()(conv_up_4)
outputs = Conv2DTranspose(3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_4)
model = tf.keras.models.Model(inputs, outputs)
return model
B. Discriminator¶
The discriminator model, represented as $D$, will take in the real image $y$ ( from the training data ) and the generated image $G(x)$ ( from the generator ) to output two probabilities.
- We train the discriminator in such a manner that is able to differentiate the real images and the generated images. So, we train the model such that $y$ produces a output of $1.0$ and $G(x)$ produces an output of $0.0$.
- Note that instead of using hard labels like $1.0$ and $0.0$, we use soft labels which are close to 1 and 0. So for a hard label of $1.0$, the soft label will be $(1 - \epsilon)$ where $\epsilon$ is picked uniformly from $( 0 , 0.1 ]$
def get_discriminator_model(ndim=64):
#activation = LeakyReLU
activation = ELU
inputs = Input(shape=(img_size, img_size, 3))
conv1 = Conv2D(ndim*2**0, kernel_size=(5, 5), strides=2, padding='same', kernel_initializer='he_normal')(inputs)
conv1 = BatchNormalization()(conv1)
conv1 = activation()(conv1)
conv1 = Conv2D(ndim*2**0, kernel_size=(5, 5), strides=2, padding='same', kernel_initializer='he_normal')(conv1)
conv1 = BatchNormalization()(conv1)
conv1 = activation()(conv1)
conv1 = MaxPooling2D()(conv1)
conv2 = Conv2D(ndim*2**1, kernel_size=(4, 4), strides=2, padding='same', kernel_initializer='he_normal')(conv1)
conv2 = BatchNormalization()(conv2)
conv2 = activation()(conv2)
conv2 = Conv2D(ndim*2**1, kernel_size=(4, 4), strides=2, padding='same', kernel_initializer='he_normal')(conv2)
conv2 = BatchNormalization()(conv2)
conv2 = activation()(conv2)
conv2 = MaxPooling2D()(conv2)
conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
conv3 = BatchNormalization()(conv3)
conv3 = activation()(conv3)
conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
conv3 = BatchNormalization()(conv3)
conv3 = activation()(conv3)
conv3 = MaxPooling2D()(conv3)
conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
conv4 = BatchNormalization()(conv4)
conv4 = activation()(conv4)
conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
conv4 = BatchNormalization()(conv4)
conv4 = activation()(conv4)
conv4 = MaxPooling2D()(conv4)
conv5 = Conv2D(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
conv5 = BatchNormalization()(conv5)
conv5 = activation()(conv5)
conv5 = Conv2D(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv5)
conv5 = BatchNormalization()(conv5)
conv5 = activation()(conv5)
fully = Flatten()(conv5)
fully = Dense(512)(fully)
fully = BatchNormalization()(fully)
fully = activation()(fully)
fully = Dense(128)(fully)
fully = BatchNormalization()(fully)
fully = activation()(fully)
fully = Dense(32)(fully)
fully = BatchNormalization()(fully)
fully = activation()(fully)
fully = Dense(1, activation='sigmoid')(fully)
model = tf.keras.models.Model(inputs, fully)
return model
def get_discriminator_model(ndim=64):
#activation = LeakyReLU
activation = ELU
inputs = Input(shape=(img_size, img_size, 3))
conv1 = Conv2D(ndim*2**0, kernel_size=(5, 5), strides=1, padding='same', kernel_initializer='he_normal')(inputs)
conv1 = BatchNormalization()(conv1)
conv1 = activation()(conv1)
conv1 = MaxPooling2D()(conv1)
conv1 = Conv2D(ndim*2**0, kernel_size=(5, 5), strides=1, padding='same', kernel_initializer='he_normal')(conv1)
conv1 = BatchNormalization()(conv1)
conv1 = activation()(conv1)
conv1 = MaxPooling2D()(conv1)
conv2 = Conv2D(ndim*2**1, kernel_size=(4, 4), strides=1, padding='same', kernel_initializer='he_normal')(conv1)
conv2 = BatchNormalization()(conv2)
conv2 = activation()(conv2)
conv2 = MaxPooling2D()(conv2)
conv2 = Conv2D(ndim*2**1, kernel_size=(4, 4), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
conv2 = BatchNormalization()(conv2)
conv2 = activation()(conv2)
conv2 = MaxPooling2D()(conv2)
conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
conv3 = BatchNormalization()(conv3)
conv3 = activation()(conv3)
conv3 = MaxPooling2D()(conv3)
conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
conv3 = BatchNormalization()(conv3)
conv3 = activation()(conv3)
conv3 = MaxPooling2D()(conv3)
conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
conv4 = BatchNormalization()(conv4)
conv4 = activation()(conv4)
conv4 = MaxPooling2D()(conv4)
conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
conv4 = BatchNormalization()(conv4)
conv4 = activation()(conv4)
fully = Flatten()(conv4)
fully = Dense(512)(fully)
fully = BatchNormalization()(fully)
fully = activation()(fully)
fully = Dense(128)(fully)
fully = BatchNormalization()(fully)
fully = activation()(fully)
fully = Dense(32)(fully)
fully = BatchNormalization()(fully)
fully = activation()(fully)
fully = Dense(1, activation='sigmoid')(fully)
model = tf.keras.models.Model(inputs, fully)
return model
C. Loss Functions¶
We'll now implement the loss functions for our GAN model. As you might know that we have two loss functions, one for the generator and another for the discriminator.
- For our generator, we'll use the L2/MSE loss function.
- For optimization, we use the Adam optimizer with a learning rate of 0.001
cross_entropy = tf.keras.losses.BinaryCrossentropy()
mse = tf.keras.losses.MeanSquaredError()
def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(tf.ones_like(real_output), real_output) # - tf.random.uniform( shape=real_output.shape , maxval=0.05) , real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output) # + tf.random.uniform( shape=fake_output.shape , maxval=0.05), fake_output)
total_loss = real_loss + fake_loss
return total_loss
def generator_loss(fake_output , real_y):
real_y = tf.cast( real_y , 'float32' )
return mse( fake_output , real_y )
generator_optimizer = tf.keras.optimizers.Adam()
discriminator_optimizer = tf.keras.optimizers.Adam()
generator = get_generator_model(ndim=32)
discriminator = get_discriminator_model(ndim=32)
# Show generator graph
tf.keras.utils.plot_model(generator, rankdir="TB")
# Show generator summary
generator.summary()
Model: "model_2" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_3 (InputLayer) [(None, 512, 512, 1) 0 __________________________________________________________________________________________________ conv2d_16 (Conv2D) (None, 512, 512, 32) 320 input_3[0][0] __________________________________________________________________________________________________ batch_normalization_27 (BatchNo (None, 512, 512, 32) 128 conv2d_16[0][0] __________________________________________________________________________________________________ elu_27 (ELU) (None, 512, 512, 32) 0 batch_normalization_27[0][0] __________________________________________________________________________________________________ max_pooling2d_14 (MaxPooling2D) (None, 256, 256, 32) 0 elu_27[0][0] __________________________________________________________________________________________________ conv2d_17 (Conv2D) (None, 256, 256, 32) 9248 max_pooling2d_14[0][0] __________________________________________________________________________________________________ batch_normalization_28 (BatchNo (None, 256, 256, 32) 128 conv2d_17[0][0] __________________________________________________________________________________________________ elu_28 (ELU) (None, 256, 256, 32) 0 batch_normalization_28[0][0] __________________________________________________________________________________________________ max_pooling2d_15 (MaxPooling2D) (None, 128, 128, 32) 0 elu_28[0][0] __________________________________________________________________________________________________ conv2d_18 (Conv2D) (None, 128, 128, 64) 18496 max_pooling2d_15[0][0] __________________________________________________________________________________________________ batch_normalization_29 (BatchNo (None, 128, 128, 64) 256 conv2d_18[0][0] __________________________________________________________________________________________________ elu_29 (ELU) (None, 128, 128, 64) 0 batch_normalization_29[0][0] __________________________________________________________________________________________________ max_pooling2d_16 (MaxPooling2D) (None, 64, 64, 64) 0 elu_29[0][0] __________________________________________________________________________________________________ conv2d_19 (Conv2D) (None, 64, 64, 64) 36928 max_pooling2d_16[0][0] __________________________________________________________________________________________________ batch_normalization_30 (BatchNo (None, 64, 64, 64) 256 conv2d_19[0][0] __________________________________________________________________________________________________ elu_30 (ELU) (None, 64, 64, 64) 0 batch_normalization_30[0][0] __________________________________________________________________________________________________ max_pooling2d_17 (MaxPooling2D) (None, 32, 32, 64) 0 elu_30[0][0] __________________________________________________________________________________________________ conv2d_20 (Conv2D) (None, 32, 32, 128) 73856 max_pooling2d_17[0][0] __________________________________________________________________________________________________ batch_normalization_31 (BatchNo (None, 32, 32, 128) 512 conv2d_20[0][0] __________________________________________________________________________________________________ elu_31 (ELU) (None, 32, 32, 128) 0 batch_normalization_31[0][0] __________________________________________________________________________________________________ max_pooling2d_18 (MaxPooling2D) (None, 16, 16, 128) 0 elu_31[0][0] __________________________________________________________________________________________________ conv2d_21 (Conv2D) (None, 16, 16, 128) 147584 max_pooling2d_18[0][0] __________________________________________________________________________________________________ batch_normalization_32 (BatchNo (None, 16, 16, 128) 512 conv2d_21[0][0] __________________________________________________________________________________________________ elu_32 (ELU) (None, 16, 16, 128) 0 batch_normalization_32[0][0] __________________________________________________________________________________________________ max_pooling2d_19 (MaxPooling2D) (None, 8, 8, 128) 0 elu_32[0][0] __________________________________________________________________________________________________ conv2d_22 (Conv2D) (None, 8, 8, 256) 295168 max_pooling2d_19[0][0] __________________________________________________________________________________________________ batch_normalization_33 (BatchNo (None, 8, 8, 256) 1024 conv2d_22[0][0] __________________________________________________________________________________________________ elu_33 (ELU) (None, 8, 8, 256) 0 batch_normalization_33[0][0] __________________________________________________________________________________________________ max_pooling2d_20 (MaxPooling2D) (None, 4, 4, 256) 0 elu_33[0][0] __________________________________________________________________________________________________ conv2d_23 (Conv2D) (None, 4, 4, 256) 590080 max_pooling2d_20[0][0] __________________________________________________________________________________________________ batch_normalization_34 (BatchNo (None, 4, 4, 256) 1024 conv2d_23[0][0] __________________________________________________________________________________________________ elu_34 (ELU) (None, 4, 4, 256) 0 batch_normalization_34[0][0] __________________________________________________________________________________________________ dropout_1 (Dropout) (None, 4, 4, 256) 0 elu_34[0][0] __________________________________________________________________________________________________ flatten_2 (Flatten) (None, 4096) 0 dropout_1[0][0] __________________________________________________________________________________________________ dense_6 (Dense) (None, 1024) 4195328 flatten_2[0][0] __________________________________________________________________________________________________ dense_7 (Dense) (None, 4096) 4198400 dense_6[0][0] __________________________________________________________________________________________________ reshape_1 (Reshape) (None, 4, 4, 256) 0 dense_7[0][0] __________________________________________________________________________________________________ conv2d_transpose_9 (Conv2DTrans (None, 4, 4, 256) 590080 reshape_1[0][0] __________________________________________________________________________________________________ concatenate_4 (Concatenate) (None, 4, 4, 512) 0 elu_34[0][0] conv2d_transpose_9[0][0] __________________________________________________________________________________________________ batch_normalization_35 (BatchNo (None, 4, 4, 512) 2048 concatenate_4[0][0] __________________________________________________________________________________________________ elu_35 (ELU) (None, 4, 4, 512) 0 batch_normalization_35[0][0] __________________________________________________________________________________________________ up_sampling2d_7 (UpSampling2D) (None, 8, 8, 512) 0 elu_35[0][0] __________________________________________________________________________________________________ conv2d_transpose_10 (Conv2DTran (None, 8, 8, 256) 1179904 up_sampling2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_36 (BatchNo (None, 8, 8, 256) 1024 conv2d_transpose_10[0][0] __________________________________________________________________________________________________ elu_36 (ELU) (None, 8, 8, 256) 0 batch_normalization_36[0][0] __________________________________________________________________________________________________ up_sampling2d_8 (UpSampling2D) (None, 16, 16, 256) 0 elu_36[0][0] __________________________________________________________________________________________________ conv2d_transpose_11 (Conv2DTran (None, 16, 16, 128) 295040 up_sampling2d_8[0][0] __________________________________________________________________________________________________ concatenate_5 (Concatenate) (None, 16, 16, 256) 0 elu_32[0][0] conv2d_transpose_11[0][0] __________________________________________________________________________________________________ batch_normalization_37 (BatchNo (None, 16, 16, 256) 1024 concatenate_5[0][0] __________________________________________________________________________________________________ elu_37 (ELU) (None, 16, 16, 256) 0 batch_normalization_37[0][0] __________________________________________________________________________________________________ up_sampling2d_9 (UpSampling2D) (None, 32, 32, 256) 0 elu_37[0][0] __________________________________________________________________________________________________ conv2d_transpose_12 (Conv2DTran (None, 32, 32, 128) 295040 up_sampling2d_9[0][0] __________________________________________________________________________________________________ batch_normalization_38 (BatchNo (None, 32, 32, 128) 512 conv2d_transpose_12[0][0] __________________________________________________________________________________________________ elu_38 (ELU) (None, 32, 32, 128) 0 batch_normalization_38[0][0] __________________________________________________________________________________________________ up_sampling2d_10 (UpSampling2D) (None, 64, 64, 128) 0 elu_38[0][0] __________________________________________________________________________________________________ conv2d_transpose_13 (Conv2DTran (None, 64, 64, 64) 73792 up_sampling2d_10[0][0] __________________________________________________________________________________________________ concatenate_6 (Concatenate) (None, 64, 64, 128) 0 elu_30[0][0] conv2d_transpose_13[0][0] __________________________________________________________________________________________________ batch_normalization_39 (BatchNo (None, 64, 64, 128) 512 concatenate_6[0][0] __________________________________________________________________________________________________ elu_39 (ELU) (None, 64, 64, 128) 0 batch_normalization_39[0][0] __________________________________________________________________________________________________ up_sampling2d_11 (UpSampling2D) (None, 128, 128, 128 0 elu_39[0][0] __________________________________________________________________________________________________ conv2d_transpose_14 (Conv2DTran (None, 128, 128, 64) 73792 up_sampling2d_11[0][0] __________________________________________________________________________________________________ batch_normalization_40 (BatchNo (None, 128, 128, 64) 256 conv2d_transpose_14[0][0] __________________________________________________________________________________________________ elu_40 (ELU) (None, 128, 128, 64) 0 batch_normalization_40[0][0] __________________________________________________________________________________________________ up_sampling2d_12 (UpSampling2D) (None, 256, 256, 64) 0 elu_40[0][0] __________________________________________________________________________________________________ conv2d_transpose_15 (Conv2DTran (None, 256, 256, 32) 18464 up_sampling2d_12[0][0] __________________________________________________________________________________________________ concatenate_7 (Concatenate) (None, 256, 256, 64) 0 elu_28[0][0] conv2d_transpose_15[0][0] __________________________________________________________________________________________________ batch_normalization_41 (BatchNo (None, 256, 256, 64) 256 concatenate_7[0][0] __________________________________________________________________________________________________ elu_41 (ELU) (None, 256, 256, 64) 0 batch_normalization_41[0][0] __________________________________________________________________________________________________ up_sampling2d_13 (UpSampling2D) (None, 512, 512, 64) 0 elu_41[0][0] __________________________________________________________________________________________________ conv2d_transpose_16 (Conv2DTran (None, 512, 512, 32) 18464 up_sampling2d_13[0][0] __________________________________________________________________________________________________ batch_normalization_42 (BatchNo (None, 512, 512, 32) 128 conv2d_transpose_16[0][0] __________________________________________________________________________________________________ elu_42 (ELU) (None, 512, 512, 32) 0 batch_normalization_42[0][0] __________________________________________________________________________________________________ conv2d_transpose_17 (Conv2DTran (None, 512, 512, 3) 867 elu_42[0][0] ================================================================================================== Total params: 12,120,451 Trainable params: 12,115,651 Non-trainable params: 4,800 __________________________________________________________________________________________________
# Show generator graph
tf.keras.utils.plot_model(discriminator, rankdir="TB")
# Show discrimniator summary
discriminator.summary()
Model: "model_3" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_4 (InputLayer) [(None, 512, 512, 3)] 0 _________________________________________________________________ conv2d_24 (Conv2D) (None, 512, 512, 32) 2432 _________________________________________________________________ batch_normalization_43 (Batc (None, 512, 512, 32) 128 _________________________________________________________________ elu_43 (ELU) (None, 512, 512, 32) 0 _________________________________________________________________ max_pooling2d_21 (MaxPooling (None, 256, 256, 32) 0 _________________________________________________________________ conv2d_25 (Conv2D) (None, 256, 256, 32) 25632 _________________________________________________________________ batch_normalization_44 (Batc (None, 256, 256, 32) 128 _________________________________________________________________ elu_44 (ELU) (None, 256, 256, 32) 0 _________________________________________________________________ max_pooling2d_22 (MaxPooling (None, 128, 128, 32) 0 _________________________________________________________________ conv2d_26 (Conv2D) (None, 128, 128, 64) 32832 _________________________________________________________________ batch_normalization_45 (Batc (None, 128, 128, 64) 256 _________________________________________________________________ elu_45 (ELU) (None, 128, 128, 64) 0 _________________________________________________________________ max_pooling2d_23 (MaxPooling (None, 64, 64, 64) 0 _________________________________________________________________ conv2d_27 (Conv2D) (None, 64, 64, 64) 65600 _________________________________________________________________ batch_normalization_46 (Batc (None, 64, 64, 64) 256 _________________________________________________________________ elu_46 (ELU) (None, 64, 64, 64) 0 _________________________________________________________________ max_pooling2d_24 (MaxPooling (None, 32, 32, 64) 0 _________________________________________________________________ conv2d_28 (Conv2D) (None, 32, 32, 128) 73856 _________________________________________________________________ batch_normalization_47 (Batc (None, 32, 32, 128) 512 _________________________________________________________________ elu_47 (ELU) (None, 32, 32, 128) 0 _________________________________________________________________ max_pooling2d_25 (MaxPooling (None, 16, 16, 128) 0 _________________________________________________________________ conv2d_29 (Conv2D) (None, 16, 16, 128) 147584 _________________________________________________________________ batch_normalization_48 (Batc (None, 16, 16, 128) 512 _________________________________________________________________ elu_48 (ELU) (None, 16, 16, 128) 0 _________________________________________________________________ max_pooling2d_26 (MaxPooling (None, 8, 8, 128) 0 _________________________________________________________________ conv2d_30 (Conv2D) (None, 8, 8, 256) 295168 _________________________________________________________________ batch_normalization_49 (Batc (None, 8, 8, 256) 1024 _________________________________________________________________ elu_49 (ELU) (None, 8, 8, 256) 0 _________________________________________________________________ max_pooling2d_27 (MaxPooling (None, 4, 4, 256) 0 _________________________________________________________________ conv2d_31 (Conv2D) (None, 4, 4, 256) 590080 _________________________________________________________________ batch_normalization_50 (Batc (None, 4, 4, 256) 1024 _________________________________________________________________ elu_50 (ELU) (None, 4, 4, 256) 0 _________________________________________________________________ flatten_3 (Flatten) (None, 4096) 0 _________________________________________________________________ dense_8 (Dense) (None, 512) 2097664 _________________________________________________________________ batch_normalization_51 (Batc (None, 512) 2048 _________________________________________________________________ elu_51 (ELU) (None, 512) 0 _________________________________________________________________ dense_9 (Dense) (None, 128) 65664 _________________________________________________________________ batch_normalization_52 (Batc (None, 128) 512 _________________________________________________________________ elu_52 (ELU) (None, 128) 0 _________________________________________________________________ dense_10 (Dense) (None, 32) 4128 _________________________________________________________________ batch_normalization_53 (Batc (None, 32) 128 _________________________________________________________________ elu_53 (ELU) (None, 32) 0 _________________________________________________________________ dense_11 (Dense) (None, 1) 33 ================================================================= Total params: 3,407,201 Trainable params: 3,403,937 Non-trainable params: 3,264 _________________________________________________________________
3. Training The GAN¶
So finally, we'll train our GAN on the dataset, we prepared earlier.
@tf.function
def train_step(input_x, real_y):
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
# Generate an image -> G( x )
generated_images = generator( input_x , training=True)
# Probability that the given image is real -> D( x )
real_output = discriminator( real_y, training=True)
# Probability that the given image is the one generated -> D( G( x ) )
generated_output = discriminator(generated_images, training=True)
# L2 Loss -> || y - G(x) ||^2
gen_loss = generator_loss( generated_images , real_y )
# Log loss for the discriminator
disc_loss = discriminator_loss( real_output, generated_output )
# Collect output
average_loss = tf.keras.backend.mean(gen_loss)
sum_loss = gen_loss + disc_loss
# Compute the gradients
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
# Optimize with Adam
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
return average_loss, sum_loss
Train Model¶
# Important parameters
batch_size = 16
num_epochs = 50
def load_img(fname):
# Load gray image and scale it to [-0.5, 0.5]
return plt.imread(fname) / 255. - 0.5
from skimage.color import *
def load_img_special(fname):
# Transform color image to different representation than RGB
return rgb2hsv(plt.imread(fname)) -0.5
#return rgb2hed(plt.imread(fname))
# Compute number of epochs which would cover whole dataset
N = np.arange(len(fx_train))
selecters = np.array_split(np.random.permutation(N), len(N)//(batch_size))
selecters = selecters * 3 # Loop through dataset x times
N_tot = len(selecters)
aloss = []
sloss = []
for s in tqdm(range(len(selecters))):
#for s in tqdm(range(num_epochs)):
# Load some images
x_train = tf.convert_to_tensor(np.array([load_img(fx_train[s]) for i in selecters[s]]))
y_train = tf.convert_to_tensor(np.array([load_img(fy_train[s]) for i in selecters[s]]))
#y_train = tf.convert_to_tensor(np.array([load_img_special(fy_train[s]) for i in selecters[s]]))
average_loss, sum_loss = train_step(x_train, y_train)
print('Batch: {:03d}/{:03d}: {:.6f} {:.6f}'.format((s+1), N_tot, average_loss, sum_loss))
aloss.append(average_loss)
sloss.append(sum_loss)
Batch: 001/4218: 13.856701 15.454262 Batch: 002/4218: 10.986278 12.817921 Batch: 003/4218: 7.454744 9.181112 Batch: 004/4218: 4.402051 6.114635 Batch: 005/4218: 3.124272 4.673289 Batch: 006/4218: 1.987583 3.624164 Batch: 007/4218: 2.161666 3.797322 Batch: 008/4218: 2.023706 3.555636 Batch: 009/4218: 1.849443 3.377069 Batch: 010/4218: 1.302416 2.799319 Batch: 011/4218: 1.394053 2.852011 Batch: 012/4218: 1.793783 3.229944 Batch: 013/4218: 1.062715 2.493350 Batch: 014/4218: 1.568413 3.055525 Batch: 015/4218: 1.371664 2.865692 Batch: 016/4218: 0.908023 2.349802 Batch: 017/4218: 0.498130 1.995643 Batch: 018/4218: 0.601359 2.048714 Batch: 019/4218: 0.931320 2.361793 Batch: 020/4218: 0.980373 2.386430 Batch: 021/4218: 0.597184 2.034515 Batch: 022/4218: 0.622238 2.025705 Batch: 023/4218: 0.764219 2.157167 Batch: 024/4218: 0.471046 1.914342 Batch: 025/4218: 0.492767 1.892964 Batch: 026/4218: 0.315369 1.726635 Batch: 027/4218: 0.404906 1.792079 Batch: 028/4218: 0.300454 1.697315 Batch: 029/4218: 0.344912 1.727081 Batch: 030/4218: 0.531135 1.906325 Batch: 031/4218: 0.301708 1.673385 Batch: 032/4218: 0.341078 1.713497 Batch: 033/4218: 0.380024 1.733419 Batch: 034/4218: 0.280259 1.663151 Batch: 035/4218: 0.605632 1.976556 Batch: 036/4218: 0.265118 1.629876 Batch: 037/4218: 0.315132 1.671332 Batch: 038/4218: 0.329761 1.684642 Batch: 039/4218: 0.180621 1.519595 Batch: 040/4218: 0.186003 1.543101 Batch: 041/4218: 0.197737 1.530772 Batch: 042/4218: 0.239804 1.582829 Batch: 043/4218: 0.281316 1.628276 Batch: 044/4218: 0.249316 1.568030 Batch: 045/4218: 0.233536 1.555366 Batch: 046/4218: 0.400754 1.716018 Batch: 047/4218: 0.296433 1.594469 Batch: 048/4218: 0.102311 1.418524 Batch: 049/4218: 0.245446 1.570299 Batch: 050/4218: 0.122285 1.422178 Batch: 051/4218: 0.202795 1.493275 Batch: 052/4218: 0.183138 1.462231 Batch: 053/4218: 0.201648 1.497308 Batch: 054/4218: 0.111485 1.374808 Batch: 055/4218: 0.154649 1.435580 Batch: 056/4218: 0.231485 1.513592 Batch: 057/4218: 0.245180 1.529755 Batch: 058/4218: 0.150571 1.439481 Batch: 059/4218: 0.205290 1.495679 Batch: 060/4218: 0.121056 1.374825 Batch: 061/4218: 0.287833 1.533249 Batch: 062/4218: 0.138111 1.386517 Batch: 063/4218: 0.161440 1.413049 Batch: 064/4218: 0.193563 1.436382 Batch: 065/4218: 0.093032 1.347794 Batch: 066/4218: 0.233084 1.467846 Batch: 067/4218: 0.107049 1.338848 Batch: 068/4218: 0.089974 1.328823 Batch: 069/4218: 0.113133 1.335953 Batch: 070/4218: 0.346550 1.561952 Batch: 071/4218: 0.171524 1.394494 Batch: 072/4218: 0.212944 1.423915 Batch: 073/4218: 0.096439 1.310811 Batch: 074/4218: 0.443668 1.659294 Batch: 075/4218: 0.342189 1.546861 Batch: 076/4218: 0.148856 1.371222 Batch: 077/4218: 0.097611 1.291675 Batch: 078/4218: 0.131865 1.321981 Batch: 079/4218: 0.163946 1.339202 Batch: 080/4218: 0.194024 1.400879 Batch: 081/4218: 0.117105 1.284898 Batch: 082/4218: 0.108041 1.283247 Batch: 083/4218: 0.101449 1.322793 Batch: 084/4218: 0.236860 1.408388 Batch: 085/4218: 0.098086 1.254466 Batch: 086/4218: 0.092960 1.247998 Batch: 087/4218: 0.091170 1.247114 Batch: 088/4218: 0.249493 1.391857 Batch: 089/4218: 0.215551 1.360325 Batch: 090/4218: 0.174228 1.326926 Batch: 091/4218: 0.091545 1.233622 Batch: 092/4218: 0.087588 1.223925 Batch: 093/4218: 0.203411 1.323055 Batch: 094/4218: 0.144001 1.277005 Batch: 095/4218: 0.105982 1.240356 Batch: 096/4218: 0.082360 1.225652 Batch: 097/4218: 0.089455 1.213207 Batch: 098/4218: 0.126595 1.236600 Batch: 099/4218: 0.086408 1.183770 Batch: 100/4218: 0.174935 1.278802 Batch: 101/4218: 0.045059 1.136256 Batch: 102/4218: 0.052257 1.157395 Batch: 103/4218: 0.107207 1.224223 Batch: 104/4218: 0.086479 1.177485 Batch: 105/4218: 0.100719 1.197317 Batch: 106/4218: 0.053857 1.133169 Batch: 107/4218: 0.053425 1.150563 Batch: 108/4218: 0.143603 1.234669 Batch: 109/4218: 0.074291 1.159427 Batch: 110/4218: 0.070703 1.152159 Batch: 111/4218: 0.175700 1.240628 Batch: 112/4218: 0.092390 1.170567 Batch: 113/4218: 0.042837 1.116817 Batch: 114/4218: 0.112269 1.178055 Batch: 115/4218: 0.099862 1.152806 Batch: 116/4218: 0.199387 1.251753 Batch: 117/4218: 0.126186 1.197196 Batch: 118/4218: 0.084184 1.128019 Batch: 119/4218: 0.054314 1.093222 Batch: 120/4218: 0.146271 1.186326 Batch: 121/4218: 0.091662 1.118148 Batch: 122/4218: 0.049296 1.079647 Batch: 123/4218: 0.098493 1.122637 Batch: 124/4218: 0.106617 1.137016 Batch: 125/4218: 0.111277 1.131712 Batch: 126/4218: 0.058384 1.068743 Batch: 127/4218: 0.119729 1.166041 Batch: 128/4218: 0.064271 1.066581 Batch: 129/4218: 0.059210 1.073128 Batch: 130/4218: 0.049630 1.066558 Batch: 131/4218: 0.090208 1.089375 Batch: 132/4218: 0.101132 1.091108 Batch: 133/4218: 0.064265 1.051494 Batch: 134/4218: 0.331313 1.311898 Batch: 135/4218: 0.240773 1.246734 Batch: 136/4218: 0.052202 1.080855 Batch: 137/4218: 0.077328 1.044426 Batch: 138/4218: 0.066444 1.027827 Batch: 139/4218: 0.119652 1.099632 Batch: 140/4218: 0.158524 1.119803 Batch: 141/4218: 0.066334 1.032057 Batch: 142/4218: 0.090871 1.060077 Batch: 143/4218: 0.042853 1.014156 Batch: 144/4218: 0.038761 0.990348 Batch: 145/4218: 0.048703 1.016891 Batch: 146/4218: 0.082583 1.024809 Batch: 147/4218: 0.138989 1.081239 Batch: 148/4218: 0.058963 1.013008 Batch: 149/4218: 0.092624 1.091620 Batch: 150/4218: 0.060424 1.029727 Batch: 151/4218: 0.156940 1.124045 Batch: 152/4218: 0.130039 1.050963 Batch: 153/4218: 0.033165 0.971299 Batch: 154/4218: 0.124945 1.050934 Batch: 155/4218: 0.074210 0.997576 Batch: 156/4218: 0.051938 0.986099 Batch: 157/4218: 0.074992 0.993705 Batch: 158/4218: 0.064497 0.997430 Batch: 159/4218: 0.078734 1.014772 Batch: 160/4218: 0.062941 0.959286 Batch: 161/4218: 0.046928 0.983028 Batch: 162/4218: 0.078865 0.984163 Batch: 163/4218: 0.045809 0.959673 Batch: 164/4218: 0.105385 0.998044 Batch: 165/4218: 0.075884 0.969138 Batch: 166/4218: 0.066567 0.969694 Batch: 167/4218: 0.078401 0.984305 Batch: 168/4218: 0.138204 1.020225 Batch: 169/4218: 0.088262 0.990757 Batch: 170/4218: 0.037277 0.927519 Batch: 171/4218: 0.027804 0.904362 Batch: 172/4218: 0.082182 0.937461 Batch: 173/4218: 0.040269 0.904915 Batch: 174/4218: 0.133354 0.988348 Batch: 175/4218: 0.101312 0.966983 Batch: 176/4218: 0.046527 0.894430 Batch: 177/4218: 0.093727 0.940682 Batch: 178/4218: 0.118475 0.993999 Batch: 179/4218: 0.029641 0.869311 Batch: 180/4218: 0.062847 0.924731 Batch: 181/4218: 0.032376 0.875225 Batch: 182/4218: 0.108886 0.960328 Batch: 183/4218: 0.054125 0.917203 Batch: 184/4218: 0.090771 0.923941 Batch: 185/4218: 0.168155 1.015887 Batch: 186/4218: 0.056112 0.899484 Batch: 187/4218: 0.128034 0.957891 Batch: 188/4218: 0.111172 0.931195 Batch: 189/4218: 0.047311 0.888503 Batch: 190/4218: 0.024712 0.851799 Batch: 191/4218: 0.167985 1.008482 Batch: 192/4218: 0.049678 0.864771 Batch: 193/4218: 0.071591 0.887584 Batch: 194/4218: 0.055182 0.859893 Batch: 195/4218: 0.052400 0.863048 Batch: 196/4218: 0.089520 0.888288 Batch: 197/4218: 0.037943 0.826063 Batch: 198/4218: 0.038038 0.836784 Batch: 199/4218: 0.030777 0.817343 Batch: 200/4218: 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Batch: 343/4218: 0.034766 0.542000 Batch: 344/4218: 0.077048 0.590461 Batch: 345/4218: 0.077626 0.608494 Batch: 346/4218: 0.028976 0.522687 Batch: 347/4218: 0.024750 0.491450 Batch: 348/4218: 0.024349 0.609526 Batch: 349/4218: 0.041196 0.588995 Batch: 350/4218: 0.030725 0.544839 Batch: 351/4218: 0.019872 0.512249 Batch: 352/4218: 0.017137 0.472333 Batch: 353/4218: 0.059990 0.513582 Batch: 354/4218: 0.036246 0.609909 Batch: 355/4218: 0.071666 0.524304 Batch: 356/4218: 0.041028 0.525423 Batch: 357/4218: 0.047644 0.487292 Batch: 358/4218: 0.047913 0.513762 Batch: 359/4218: 0.029818 0.473574 Batch: 360/4218: 0.069502 0.527130 Batch: 361/4218: 0.038691 0.497575 Batch: 362/4218: 0.127165 0.585395 Batch: 363/4218: 0.026837 0.559304 Batch: 364/4218: 0.041754 0.510631 Batch: 365/4218: 0.101476 0.567239 Batch: 366/4218: 0.023372 0.455512 Batch: 367/4218: 0.037481 0.452655 Batch: 368/4218: 0.051915 0.490963 Batch: 369/4218: 0.055099 0.493270 Batch: 370/4218: 0.019741 0.449962 Batch: 371/4218: 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Batch: 457/4218: 0.052005 0.355874 Batch: 458/4218: 0.021425 0.349895 Batch: 459/4218: 0.053178 0.393145 Batch: 460/4218: 0.034137 0.333171 Batch: 461/4218: 0.020572 0.332537 Batch: 462/4218: 0.029884 0.323750 Batch: 463/4218: 0.134368 0.436595 Batch: 464/4218: 0.036219 0.336945 Batch: 465/4218: 0.025647 0.303361 Batch: 466/4218: 0.066326 0.355968 Batch: 467/4218: 0.156138 0.480860 Batch: 468/4218: 0.020887 0.320537 Batch: 469/4218: 0.032030 0.338577 Batch: 470/4218: 0.016611 0.318913 Batch: 471/4218: 0.164376 0.475411 Batch: 472/4218: 0.063436 0.373734 Batch: 473/4218: 0.094818 0.388641 Batch: 474/4218: 0.039776 0.372967 Batch: 475/4218: 0.023928 0.350587 Batch: 476/4218: 0.065861 0.405515 Batch: 477/4218: 0.124855 0.436742 Batch: 478/4218: 0.034131 0.353389 Batch: 479/4218: 0.018371 0.299512 Batch: 480/4218: 0.120680 0.395454 Batch: 481/4218: 0.024326 0.364964 Batch: 482/4218: 0.032814 0.301656 Batch: 483/4218: 0.058845 0.338724 Batch: 484/4218: 0.036654 0.308638 Batch: 485/4218: 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Batch: 571/4218: 0.065802 0.265458 Batch: 572/4218: 0.115232 0.329949 Batch: 573/4218: 0.091332 0.296759 Batch: 574/4218: 0.029852 0.263328 Batch: 575/4218: 0.032891 0.228668 Batch: 576/4218: 0.045805 0.258928 Batch: 577/4218: 0.080173 0.275510 Batch: 578/4218: 0.071286 0.321292 Batch: 579/4218: 0.058002 0.248624 Batch: 580/4218: 0.013194 0.257758 Batch: 581/4218: 0.024898 0.217445 Batch: 582/4218: 0.039948 0.249051 Batch: 583/4218: 0.029892 0.283862 Batch: 584/4218: 0.026045 0.226779 Batch: 585/4218: 0.022613 0.224326 Batch: 586/4218: 0.115028 0.311760 Batch: 587/4218: 0.022768 0.220144 Batch: 588/4218: 0.022550 0.236854 Batch: 589/4218: 0.022681 0.225351 Batch: 590/4218: 0.023687 0.225824 Batch: 591/4218: 0.064361 0.301493 Batch: 592/4218: 0.032717 0.257384 Batch: 593/4218: 0.033425 0.222805 Batch: 594/4218: 0.133248 0.324308 Batch: 595/4218: 0.029316 0.252560 Batch: 596/4218: 0.014587 0.207110 Batch: 597/4218: 0.021136 0.218578 Batch: 598/4218: 0.029232 0.210991 Batch: 599/4218: 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Batch: 628/4218: 0.039479 0.224850 Batch: 629/4218: 0.133723 0.301209 Batch: 630/4218: 0.037666 0.199483 Batch: 631/4218: 0.013144 0.186889 Batch: 632/4218: 0.017758 0.183000 Batch: 633/4218: 0.192638 0.355373 Batch: 634/4218: 0.027405 0.243077 Batch: 635/4218: 0.021121 0.184867 Batch: 636/4218: 0.030996 0.204688 Batch: 637/4218: 0.133230 0.309471 Batch: 638/4218: 0.059750 0.213033 Batch: 639/4218: 0.074320 0.241017 Batch: 640/4218: 0.022458 0.175427 Batch: 641/4218: 0.032228 0.191384 Batch: 642/4218: 0.289373 0.467930 Batch: 643/4218: 0.020476 0.184630 Batch: 644/4218: 0.016208 0.168035 Batch: 645/4218: 0.108422 0.263559 Batch: 646/4218: 0.148635 0.303987 Batch: 647/4218: 0.022625 0.176219 Batch: 648/4218: 0.030200 0.210577 Batch: 649/4218: 0.044786 0.214733 Batch: 650/4218: 0.021419 0.180666 Batch: 651/4218: 0.096662 0.259788 Batch: 652/4218: 0.021073 0.177357 Batch: 653/4218: 0.019165 0.188473 Batch: 654/4218: 0.027127 0.173385 Batch: 655/4218: 0.041717 0.191027 Batch: 656/4218: 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Batch: 799/4218: 0.032681 0.132714 Batch: 800/4218: 0.040429 0.138819 Batch: 801/4218: 0.093574 0.193869 Batch: 802/4218: 0.125453 0.223177 Batch: 803/4218: 0.031946 0.133434 Batch: 804/4218: 0.087448 0.184490 Batch: 805/4218: 0.020094 0.113241 Batch: 806/4218: 0.011819 0.109013 Batch: 807/4218: 0.037596 0.154375 Batch: 808/4218: 0.013803 0.123540 Batch: 809/4218: 0.014749 0.125450 Batch: 810/4218: 0.016055 0.109857 Batch: 811/4218: 0.035962 0.134785 Batch: 812/4218: 0.044541 0.140933 Batch: 813/4218: 0.037757 0.145884 Batch: 814/4218: 0.016604 0.115754 Batch: 815/4218: 0.157820 0.261003 Batch: 816/4218: 0.015884 0.130349 Batch: 817/4218: 0.010820 0.113903 Batch: 818/4218: 0.066222 0.162578 Batch: 819/4218: 0.021902 0.120227 Batch: 820/4218: 0.036352 0.130114 Batch: 821/4218: 0.021184 0.114858 Batch: 822/4218: 0.015496 0.111809 Batch: 823/4218: 0.048632 0.155165 Batch: 824/4218: 0.170108 0.275541 Batch: 825/4218: 0.045546 0.141059 Batch: 826/4218: 0.020643 0.107029 Batch: 827/4218: 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Batch: 856/4218: 0.081890 0.206575 Batch: 857/4218: 0.012774 0.093422 Batch: 858/4218: 0.030247 0.117094 Batch: 859/4218: 0.018763 0.102087 Batch: 860/4218: 0.043755 0.126805 Batch: 861/4218: 0.115529 0.214366 Batch: 862/4218: 0.017000 0.110789 Batch: 863/4218: 0.030568 0.118805 Batch: 864/4218: 0.033767 0.121836 Batch: 865/4218: 0.030430 0.128579 Batch: 866/4218: 0.055055 0.143377 Batch: 867/4218: 0.025229 0.106043 Batch: 868/4218: 0.041094 0.140466 Batch: 869/4218: 0.047252 0.126841 Batch: 870/4218: 0.020911 0.106927 Batch: 871/4218: 0.076012 0.187028 Batch: 872/4218: 0.022521 0.108531 Batch: 873/4218: 0.023797 0.111617 Batch: 874/4218: 0.029305 0.109643 Batch: 875/4218: 0.040164 0.125796 Batch: 876/4218: 0.026753 0.108407 Batch: 877/4218: 0.036068 0.117261 Batch: 878/4218: 0.077295 0.159562 Batch: 879/4218: 0.014169 0.091771 Batch: 880/4218: 0.010336 0.101145 Batch: 881/4218: 0.026315 0.103905 Batch: 882/4218: 0.082384 0.161149 Batch: 883/4218: 0.031181 0.121889 Batch: 884/4218: 0.024146 0.117293 Batch: 885/4218: 0.029560 0.104522 Batch: 886/4218: 0.040942 0.117141 Batch: 887/4218: 0.020497 0.114502 Batch: 888/4218: 0.019517 0.113175 Batch: 889/4218: 0.064706 0.155677 Batch: 890/4218: 0.021460 0.132997 Batch: 891/4218: 0.015138 0.104640 Batch: 892/4218: 0.013217 0.096954 Batch: 893/4218: 0.010978 0.083849 Batch: 894/4218: 0.064662 0.144407 Batch: 895/4218: 0.038542 0.115518 Batch: 896/4218: 0.055925 0.135275 Batch: 897/4218: 0.129370 0.215262 Batch: 898/4218: 0.045925 0.124875 Batch: 899/4218: 0.054553 0.138996 Batch: 900/4218: 0.038362 0.113016 Batch: 901/4218: 0.050397 0.128327 Batch: 902/4218: 0.029504 0.101891 Batch: 903/4218: 0.062579 0.132391 Batch: 904/4218: 0.029297 0.103482 Batch: 905/4218: 0.031581 0.115594 Batch: 906/4218: 0.031580 0.128201 Batch: 907/4218: 0.043415 0.121436 Batch: 908/4218: 0.036053 0.120657 Batch: 909/4218: 0.020867 0.098357 Batch: 910/4218: 0.026995 0.129107 Batch: 911/4218: 0.025937 0.115359 Batch: 912/4218: 0.047871 0.152357 Batch: 913/4218: 0.043519 0.178785 Batch: 914/4218: 0.072954 0.154726 Batch: 915/4218: 0.024985 0.105702 Batch: 916/4218: 0.022872 0.097870 Batch: 917/4218: 0.075925 0.151055 Batch: 918/4218: 0.042442 0.131329 Batch: 919/4218: 0.016065 0.102273 Batch: 920/4218: 0.028656 0.094903 Batch: 921/4218: 0.056038 0.130118 Batch: 922/4218: 0.031828 0.107835 Batch: 923/4218: 0.016853 0.082226 Batch: 924/4218: 0.025894 0.099794 Batch: 925/4218: 0.022329 0.107137 Batch: 926/4218: 0.035509 0.112807 Batch: 927/4218: 0.231675 0.313464 Batch: 928/4218: 0.051380 0.152957 Batch: 929/4218: 0.121568 0.197745 Batch: 930/4218: 0.010700 0.084392 Batch: 931/4218: 0.024031 0.094150 Batch: 932/4218: 0.024557 0.101589 Batch: 933/4218: 0.032930 0.102636 Batch: 934/4218: 0.077966 0.147799 Batch: 935/4218: 0.063371 0.142299 Batch: 936/4218: 0.034526 0.101497 Batch: 937/4218: 0.026409 0.089226 Batch: 938/4218: 0.013957 0.089837 Batch: 939/4218: 0.026107 0.088599 Batch: 940/4218: 0.009051 0.088080 Batch: 941/4218: 0.038503 0.110232 Batch: 942/4218: 0.011237 0.074240 Batch: 943/4218: 0.027341 0.091002 Batch: 944/4218: 0.051143 0.122985 Batch: 945/4218: 0.039395 0.103909 Batch: 946/4218: 0.011874 0.076330 Batch: 947/4218: 0.033860 0.097520 Batch: 948/4218: 0.045875 0.124521 Batch: 949/4218: 0.054843 0.138428 Batch: 950/4218: 0.034374 0.114942 Batch: 951/4218: 0.064089 0.138355 Batch: 952/4218: 0.136191 0.202243 Batch: 953/4218: 0.042646 0.125200 Batch: 954/4218: 0.021808 0.084942 Batch: 955/4218: 0.034416 0.115124 Batch: 956/4218: 0.055337 0.128940 Batch: 957/4218: 0.047711 0.115106 Batch: 958/4218: 0.008965 0.070687 Batch: 959/4218: 0.035295 0.099621 Batch: 960/4218: 0.055726 0.126992 Batch: 961/4218: 0.126495 0.189844 Batch: 962/4218: 0.027398 0.095464 Batch: 963/4218: 0.014845 0.084454 Batch: 964/4218: 0.017374 0.083568 Batch: 965/4218: 0.025845 0.092467 Batch: 966/4218: 0.031489 0.096512 Batch: 967/4218: 0.022762 0.093579 Batch: 968/4218: 0.016955 0.084839 Batch: 969/4218: 0.012477 0.083705 Batch: 970/4218: 0.019918 0.086157 Batch: 971/4218: 0.034215 0.099577 Batch: 972/4218: 0.013927 0.077687 Batch: 973/4218: 0.023766 0.097138 Batch: 974/4218: 0.035092 0.097407 Batch: 975/4218: 0.019758 0.079650 Batch: 976/4218: 0.017822 0.075025 Batch: 977/4218: 0.016187 0.078000 Batch: 978/4218: 0.046375 0.113258 Batch: 979/4218: 0.009024 0.070740 Batch: 980/4218: 0.019652 0.084489 Batch: 981/4218: 0.029415 0.093865 Batch: 982/4218: 0.018473 0.076122 Batch: 983/4218: 0.022820 0.083086 Batch: 984/4218: 0.043965 0.100272 Batch: 985/4218: 0.012614 0.077060 Batch: 986/4218: 0.011446 0.079660 Batch: 987/4218: 0.051999 0.123587 Batch: 988/4218: 0.032331 0.092503 Batch: 989/4218: 0.049096 0.108381 Batch: 990/4218: 0.018417 0.081211 Batch: 991/4218: 0.008611 0.074427 Batch: 992/4218: 0.014035 0.077639 Batch: 993/4218: 0.034075 0.105465 Batch: 994/4218: 0.120078 0.185215 Batch: 995/4218: 0.023423 0.101088 Batch: 996/4218: 0.044061 0.116954 Batch: 997/4218: 0.030080 0.109673 Batch: 998/4218: 0.047486 0.113062 Batch: 999/4218: 0.061102 0.147432 Batch: 1000/4218: 0.022785 0.083822 Batch: 1001/4218: 0.036829 0.102930 Batch: 1002/4218: 0.052908 0.121525 Batch: 1003/4218: 0.021448 0.074619 Batch: 1004/4218: 0.037956 0.107651 Batch: 1005/4218: 0.103814 0.185436 Batch: 1006/4218: 0.015815 0.069647 Batch: 1007/4218: 0.021232 0.083320 Batch: 1008/4218: 0.025791 0.080912 Batch: 1009/4218: 0.038408 0.121408 Batch: 1010/4218: 0.169408 0.225257 Batch: 1011/4218: 0.029828 0.088319 Batch: 1012/4218: 0.086366 0.143684 Batch: 1013/4218: 0.033591 0.095483 Batch: 1014/4218: 0.014703 0.077681 Batch: 1015/4218: 0.031275 0.088000 Batch: 1016/4218: 0.043393 0.101934 Batch: 1017/4218: 0.019490 0.090537 Batch: 1018/4218: 0.015133 0.074001 Batch: 1019/4218: 0.022573 0.075696 Batch: 1020/4218: 0.037630 0.103317 Batch: 1021/4218: 0.058126 0.113465 Batch: 1022/4218: 0.120622 0.176414 Batch: 1023/4218: 0.027078 0.083140 Batch: 1024/4218: 0.019892 0.074722 Batch: 1025/4218: 0.036845 0.091006 Batch: 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Batch: 1054/4218: 0.017263 0.069759 Batch: 1055/4218: 0.021210 0.070136 Batch: 1056/4218: 0.035528 0.094329 Batch: 1057/4218: 0.014672 0.065846 Batch: 1058/4218: 0.038457 0.090791 Batch: 1059/4218: 0.023204 0.072781 Batch: 1060/4218: 0.019751 0.083120 Batch: 1061/4218: 0.165484 0.215205 Batch: 1062/4218: 0.113219 0.162640 Batch: 1063/4218: 0.037261 0.084574 Batch: 1064/4218: 0.026063 0.073487 Batch: 1065/4218: 0.012288 0.065802 Batch: 1066/4218: 0.021648 0.070381 Batch: 1067/4218: 0.071006 0.117616 Batch: 1068/4218: 0.043550 0.092824 Batch: 1069/4218: 0.014266 0.063562 Batch: 1070/4218: 0.025990 0.078411 Batch: 1071/4218: 0.065354 0.113084 Batch: 1072/4218: 0.046607 0.093787 Batch: 1073/4218: 0.024405 0.079776 Batch: 1074/4218: 0.029040 0.088239 Batch: 1075/4218: 0.019947 0.066739 Batch: 1076/4218: 0.040736 0.085735 Batch: 1077/4218: 0.037138 0.088350 Batch: 1078/4218: 0.128116 0.191194 Batch: 1079/4218: 0.028989 0.080644 Batch: 1080/4218: 0.038497 0.104875 Batch: 1081/4218: 0.120401 0.168622 Batch: 1082/4218: 0.048268 0.103780 Batch: 1083/4218: 0.032018 0.085022 Batch: 1084/4218: 0.069415 0.120639 Batch: 1085/4218: 0.023907 0.068051 Batch: 1086/4218: 0.087806 0.140610 Batch: 1087/4218: 0.026996 0.089301 Batch: 1088/4218: 0.055241 0.109126 Batch: 1089/4218: 0.020222 0.065573 Batch: 1090/4218: 0.026248 0.073427 Batch: 1091/4218: 0.012226 0.074877 Batch: 1092/4218: 0.044203 0.092803 Batch: 1093/4218: 0.097379 0.145119 Batch: 1094/4218: 0.028854 0.085590 Batch: 1095/4218: 0.076142 0.135805 Batch: 1096/4218: 0.025465 0.073354 Batch: 1097/4218: 0.053125 0.096390 Batch: 1098/4218: 0.025220 0.072119 Batch: 1099/4218: 0.017386 0.065893 Batch: 1100/4218: 0.093130 0.148424 Batch: 1101/4218: 0.089500 0.136118 Batch: 1102/4218: 0.023611 0.066471 Batch: 1103/4218: 0.060641 0.109547 Batch: 1104/4218: 0.013534 0.067733 Batch: 1105/4218: 0.055998 0.105429 Batch: 1106/4218: 0.026485 0.074980 Batch: 1107/4218: 0.078509 0.122067 Batch: 1108/4218: 0.011825 0.062535 Batch: 1109/4218: 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Batch: 1165/4218: 0.047635 0.083058 Batch: 1166/4218: 0.010981 0.053352 Batch: 1167/4218: 0.063088 0.099745 Batch: 1168/4218: 0.005581 0.054509 Batch: 1169/4218: 0.016991 0.058424 Batch: 1170/4218: 0.010629 0.054993 Batch: 1171/4218: 0.007292 0.048320 Batch: 1172/4218: 0.046390 0.088043 Batch: 1173/4218: 0.010788 0.052047 Batch: 1174/4218: 0.027512 0.066356 Batch: 1175/4218: 0.024541 0.075944 Batch: 1176/4218: 0.025530 0.064522 Batch: 1177/4218: 0.027919 0.072981 Batch: 1178/4218: 0.027775 0.092115 Batch: 1179/4218: 0.114505 0.151889 Batch: 1180/4218: 0.091342 0.128637 Batch: 1181/4218: 0.035209 0.073018 Batch: 1182/4218: 0.019479 0.057397 Batch: 1183/4218: 0.074795 0.143843 Batch: 1184/4218: 0.017034 0.064912 Batch: 1185/4218: 0.043997 0.087613 Batch: 1186/4218: 0.033305 0.076206 Batch: 1187/4218: 0.010130 0.057225 Batch: 1188/4218: 0.037703 0.073296 Batch: 1189/4218: 0.012864 0.051633 Batch: 1190/4218: 0.021271 0.066009 Batch: 1191/4218: 0.013854 0.053536 Batch: 1192/4218: 0.029540 0.075065 Batch: 1193/4218: 0.016716 0.054441 Batch: 1194/4218: 0.018000 0.053625 Batch: 1195/4218: 0.004130 0.041544 Batch: 1196/4218: 0.023734 0.068149 Batch: 1197/4218: 0.020214 0.057550 Batch: 1198/4218: 0.057669 0.101317 Batch: 1199/4218: 0.045817 0.085373 Batch: 1200/4218: 0.008255 0.056833 Batch: 1201/4218: 0.130724 0.169989 Batch: 1202/4218: 0.023131 0.065945 Batch: 1203/4218: 0.024805 0.091523 Batch: 1204/4218: 0.018915 0.056966 Batch: 1205/4218: 0.020805 0.063601 Batch: 1206/4218: 0.094516 0.136698 Batch: 1207/4218: 0.046552 0.087449 Batch: 1208/4218: 0.013777 0.050024 Batch: 1209/4218: 0.087743 0.128756 Batch: 1210/4218: 0.035470 0.074936 Batch: 1211/4218: 0.108971 0.149557 Batch: 1212/4218: 0.011914 0.045027 Batch: 1213/4218: 0.025642 0.063477 Batch: 1214/4218: 0.012452 0.051036 Batch: 1215/4218: 0.024023 0.060221 Batch: 1216/4218: 0.054989 0.092200 Batch: 1217/4218: 0.018762 0.053228 Batch: 1218/4218: 0.014933 0.053957 Batch: 1219/4218: 0.025219 0.058455 Batch: 1220/4218: 0.024406 0.063881 Batch: 1221/4218: 0.009734 0.046111 Batch: 1222/4218: 0.020164 0.051798 Batch: 1223/4218: 0.030542 0.070337 Batch: 1224/4218: 0.022664 0.058136 Batch: 1225/4218: 0.103262 0.145000 Batch: 1226/4218: 0.015594 0.051019 Batch: 1227/4218: 0.057501 0.101647 Batch: 1228/4218: 0.014466 0.046786 Batch: 1229/4218: 0.013022 0.053361 Batch: 1230/4218: 0.008251 0.039833 Batch: 1231/4218: 0.025137 0.072432 Batch: 1232/4218: 0.033118 0.067370 Batch: 1233/4218: 0.029818 0.062377 Batch: 1234/4218: 0.044717 0.078835 Batch: 1235/4218: 0.016600 0.047284 Batch: 1236/4218: 0.012603 0.050091 Batch: 1237/4218: 0.220678 0.253763 Batch: 1238/4218: 0.018723 0.058580 Batch: 1239/4218: 0.157314 0.192871 Batch: 1240/4218: 0.020299 0.066938 Batch: 1241/4218: 0.126471 0.160760 Batch: 1242/4218: 0.022109 0.055847 Batch: 1243/4218: 0.019965 0.070103 Batch: 1244/4218: 0.020632 0.099090 Batch: 1245/4218: 0.031366 0.076696 Batch: 1246/4218: 0.064985 0.103787 Batch: 1247/4218: 0.023401 0.056408 Batch: 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Batch: 1276/4218: 0.024647 0.056109 Batch: 1277/4218: 0.052613 0.086245 Batch: 1278/4218: 0.052057 0.095588 Batch: 1279/4218: 0.032248 0.064045 Batch: 1280/4218: 0.015229 0.046372 Batch: 1281/4218: 0.050084 0.081564 Batch: 1282/4218: 0.051832 0.097840 Batch: 1283/4218: 0.068510 0.099526 Batch: 1284/4218: 0.055793 0.086035 Batch: 1285/4218: 0.028878 0.092404 Batch: 1286/4218: 0.064979 0.092398 Batch: 1287/4218: 0.039814 0.071440 Batch: 1288/4218: 0.045002 0.078518 Batch: 1289/4218: 0.084831 0.117209 Batch: 1290/4218: 0.038675 0.075921 Batch: 1291/4218: 0.030226 0.064861 Batch: 1292/4218: 0.025910 0.061453 Batch: 1293/4218: 0.026096 0.059250 Batch: 1294/4218: 0.017022 0.057553 Batch: 1295/4218: 0.035852 0.065427 Batch: 1296/4218: 0.063538 0.117702 Batch: 1297/4218: 0.007997 0.040477 Batch: 1298/4218: 0.014428 0.047259 Batch: 1299/4218: 0.022777 0.061458 Batch: 1300/4218: 0.029591 0.060927 Batch: 1301/4218: 0.014658 0.047425 Batch: 1302/4218: 0.020110 0.053258 Batch: 1303/4218: 0.080503 0.114163 Batch: 1304/4218: 0.070185 0.101071 Batch: 1305/4218: 0.035335 0.064497 Batch: 1306/4218: 0.059356 0.089123 Batch: 1307/4218: 0.028653 0.064855 Batch: 1308/4218: 0.058984 0.089323 Batch: 1309/4218: 0.024934 0.062776 Batch: 1310/4218: 0.018667 0.047202 Batch: 1311/4218: 0.027008 0.085006 Batch: 1312/4218: 0.032584 0.062550 Batch: 1313/4218: 0.007791 0.046334 Batch: 1314/4218: 0.015882 0.051701 Batch: 1315/4218: 0.019554 0.046510 Batch: 1316/4218: 0.013165 0.042342 Batch: 1317/4218: 0.022824 0.056000 Batch: 1318/4218: 0.033597 0.074046 Batch: 1319/4218: 0.013186 0.048628 Batch: 1320/4218: 0.012052 0.046508 Batch: 1321/4218: 0.032285 0.062059 Batch: 1322/4218: 0.021478 0.060392 Batch: 1323/4218: 0.019044 0.062072 Batch: 1324/4218: 0.020923 0.052058 Batch: 1325/4218: 0.020151 0.050645 Batch: 1326/4218: 0.015698 0.047836 Batch: 1327/4218: 0.042677 0.070342 Batch: 1328/4218: 0.018631 0.060434 Batch: 1329/4218: 0.027999 0.058056 Batch: 1330/4218: 0.012796 0.052806 Batch: 1331/4218: 0.056031 0.086393 Batch: 1332/4218: 0.052817 0.087279 Batch: 1333/4218: 0.095947 0.127978 Batch: 1334/4218: 0.035439 0.064675 Batch: 1335/4218: 0.026942 0.061335 Batch: 1336/4218: 0.024416 0.052115 Batch: 1337/4218: 0.024200 0.053045 Batch: 1338/4218: 0.022758 0.050685 Batch: 1339/4218: 0.038057 0.075592 Batch: 1340/4218: 0.078656 0.108324 Batch: 1341/4218: 0.013821 0.050279 Batch: 1342/4218: 0.011822 0.048578 Batch: 1343/4218: 0.024312 0.053576 Batch: 1344/4218: 0.019555 0.050917 Batch: 1345/4218: 0.024858 0.053227 Batch: 1346/4218: 0.018886 0.050898 Batch: 1347/4218: 0.032761 0.062311 Batch: 1348/4218: 0.005219 0.036064 Batch: 1349/4218: 0.014161 0.041929 Batch: 1350/4218: 0.006253 0.032624 Batch: 1351/4218: 0.022544 0.051386 Batch: 1352/4218: 0.030707 0.056261 Batch: 1353/4218: 0.143672 0.177659 Batch: 1354/4218: 0.012527 0.041667 Batch: 1355/4218: 0.015749 0.045457 Batch: 1356/4218: 0.035703 0.070036 Batch: 1357/4218: 0.076302 0.106685 Batch: 1358/4218: 0.050836 0.078597 Batch: 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Batch: 1387/4218: 0.040170 0.070280 Batch: 1388/4218: 0.017046 0.043388 Batch: 1389/4218: 0.021619 0.043808 Batch: 1390/4218: 0.010241 0.033713 Batch: 1391/4218: 0.038394 0.066214 Batch: 1392/4218: 0.032924 0.067777 Batch: 1393/4218: 0.009039 0.036745 Batch: 1394/4218: 0.044252 0.068468 Batch: 1395/4218: 0.028135 0.054724 Batch: 1396/4218: 0.060540 0.094405 Batch: 1397/4218: 0.037485 0.062591 Batch: 1398/4218: 0.037554 0.064600 Batch: 1399/4218: 0.009019 0.032870 Batch: 1400/4218: 0.034415 0.058297 Batch: 1401/4218: 0.031426 0.056167 Batch: 1402/4218: 0.016858 0.044383 Batch: 1403/4218: 0.022783 0.047631 Batch: 1404/4218: 0.014000 0.037321 Batch: 1405/4218: 0.019879 0.042162 Batch: 1406/4218: 0.008304 0.032351 Batch: 1407/4218: 0.077896 0.104040 Batch: 1408/4218: 0.011301 0.036575 Batch: 1409/4218: 0.039645 0.065504 Batch: 1410/4218: 0.013585 0.046606 Batch: 1411/4218: 0.046276 0.080057 Batch: 1412/4218: 0.010800 0.034501 Batch: 1413/4218: 0.021662 0.045873 Batch: 1414/4218: 0.090763 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0.061858 0.083501 Batch: 1443/4218: 0.024276 0.047004 Batch: 1444/4218: 0.070445 0.099375 Batch: 1445/4218: 0.049187 0.074512 Batch: 1446/4218: 0.011633 0.034199 Batch: 1447/4218: 0.078268 0.102534 Batch: 1448/4218: 0.030619 0.055695 Batch: 1449/4218: 0.018182 0.044265 Batch: 1450/4218: 0.033756 0.058613 Batch: 1451/4218: 0.021977 0.047497 Batch: 1452/4218: 0.013186 0.037147 Batch: 1453/4218: 0.026646 0.049703 Batch: 1454/4218: 0.012794 0.039774 Batch: 1455/4218: 0.040278 0.061366 Batch: 1456/4218: 0.046785 0.072520 Batch: 1457/4218: 0.013659 0.035145 Batch: 1458/4218: 0.023183 0.059852 Batch: 1459/4218: 0.022180 0.046855 Batch: 1460/4218: 0.015320 0.039306 Batch: 1461/4218: 0.014395 0.047659 Batch: 1462/4218: 0.047411 0.071683 Batch: 1463/4218: 0.034889 0.055160 Batch: 1464/4218: 0.068693 0.099641 Batch: 1465/4218: 0.039160 0.076768 Batch: 1466/4218: 0.054821 0.077134 Batch: 1467/4218: 0.238781 0.272787 Batch: 1468/4218: 0.059660 0.082198 Batch: 1469/4218: 0.014640 0.037721 Batch: 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Batch: 1498/4218: 0.141628 0.173761 Batch: 1499/4218: 0.083985 0.107283 Batch: 1500/4218: 0.071588 0.099659 Batch: 1501/4218: 0.026274 0.051773 Batch: 1502/4218: 0.011407 0.033518 Batch: 1503/4218: 0.057674 0.080433 Batch: 1504/4218: 0.044649 0.065655 Batch: 1505/4218: 0.023803 0.050344 Batch: 1506/4218: 0.035506 0.062410 Batch: 1507/4218: 0.068842 0.090970 Batch: 1508/4218: 0.063172 0.081481 Batch: 1509/4218: 0.019058 0.038413 Batch: 1510/4218: 0.053366 0.076097 Batch: 1511/4218: 0.021577 0.039834 Batch: 1512/4218: 0.023090 0.045235 Batch: 1513/4218: 0.026550 0.058745 Batch: 1514/4218: 0.038133 0.058625 Batch: 1515/4218: 0.015513 0.037832 Batch: 1516/4218: 0.065701 0.085298 Batch: 1517/4218: 0.061185 0.084401 Batch: 1518/4218: 0.039469 0.058458 Batch: 1519/4218: 0.036648 0.061497 Batch: 1520/4218: 0.008895 0.029910 Batch: 1521/4218: 0.021152 0.040752 Batch: 1522/4218: 0.032162 0.061606 Batch: 1523/4218: 0.047585 0.069224 Batch: 1524/4218: 0.028486 0.051209 Batch: 1525/4218: 0.020697 0.040013 Batch: 1526/4218: 0.026549 0.072783 Batch: 1527/4218: 0.026158 0.048621 Batch: 1528/4218: 0.028170 0.049732 Batch: 1529/4218: 0.040743 0.063770 Batch: 1530/4218: 0.012617 0.032475 Batch: 1531/4218: 0.024227 0.045561 Batch: 1532/4218: 0.019370 0.039827 Batch: 1533/4218: 0.029466 0.053740 Batch: 1534/4218: 0.012319 0.031521 Batch: 1535/4218: 0.037552 0.056149 Batch: 1536/4218: 0.023613 0.057553 Batch: 1537/4218: 0.030495 0.050248 Batch: 1538/4218: 0.026799 0.050150 Batch: 1539/4218: 0.017454 0.037564 Batch: 1540/4218: 0.051743 0.069884 Batch: 1541/4218: 0.014183 0.038677 Batch: 1542/4218: 0.022447 0.040818 Batch: 1543/4218: 0.022593 0.041361 Batch: 1544/4218: 0.023674 0.041410 Batch: 1545/4218: 0.071031 0.094573 Batch: 1546/4218: 0.053816 0.074466 Batch: 1547/4218: 0.028547 0.048485 Batch: 1548/4218: 0.045844 0.062945 Batch: 1549/4218: 0.092512 0.119406 Batch: 1550/4218: 0.018611 0.039441 Batch: 1551/4218: 0.049392 0.068198 Batch: 1552/4218: 0.031174 0.052383 Batch: 1553/4218: 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Batch: 1609/4218: 0.020565 0.036147 Batch: 1610/4218: 0.045743 0.065574 Batch: 1611/4218: 0.012380 0.030874 Batch: 1612/4218: 0.036772 0.053316 Batch: 1613/4218: 0.061795 0.079088 Batch: 1614/4218: 0.026317 0.045797 Batch: 1615/4218: 0.018687 0.039203 Batch: 1616/4218: 0.014201 0.029834 Batch: 1617/4218: 0.020480 0.038075 Batch: 1618/4218: 0.021478 0.037671 Batch: 1619/4218: 0.149291 0.166402 Batch: 1620/4218: 0.049257 0.066804 Batch: 1621/4218: 0.022752 0.038422 Batch: 1622/4218: 0.032091 0.050272 Batch: 1623/4218: 0.023983 0.041906 Batch: 1624/4218: 0.025016 0.042807 Batch: 1625/4218: 0.037198 0.061129 Batch: 1626/4218: 0.008510 0.023883 Batch: 1627/4218: 0.038186 0.055774 Batch: 1628/4218: 0.010841 0.030733 Batch: 1629/4218: 0.014976 0.033905 Batch: 1630/4218: 0.013783 0.033358 Batch: 1631/4218: 0.016682 0.034690 Batch: 1632/4218: 0.023698 0.040300 Batch: 1633/4218: 0.017626 0.041382 Batch: 1634/4218: 0.021809 0.039148 Batch: 1635/4218: 0.038476 0.055425 Batch: 1636/4218: 0.057310 0.074894 Batch: 1637/4218: 0.044151 0.060580 Batch: 1638/4218: 0.031790 0.050358 Batch: 1639/4218: 0.059565 0.075815 Batch: 1640/4218: 0.040559 0.059407 Batch: 1641/4218: 0.019019 0.035080 Batch: 1642/4218: 0.025098 0.043853 Batch: 1643/4218: 0.015478 0.032711 Batch: 1644/4218: 0.016736 0.032066 Batch: 1645/4218: 0.019378 0.038336 Batch: 1646/4218: 0.030923 0.046083 Batch: 1647/4218: 0.099706 0.119642 Batch: 1648/4218: 0.017279 0.037934 Batch: 1649/4218: 0.021375 0.038826 Batch: 1650/4218: 0.047853 0.064169 Batch: 1651/4218: 0.015838 0.032687 Batch: 1652/4218: 0.038377 0.053813 Batch: 1653/4218: 0.038352 0.052981 Batch: 1654/4218: 0.009608 0.025534 Batch: 1655/4218: 0.011865 0.030035 Batch: 1656/4218: 0.020175 0.035119 Batch: 1657/4218: 0.037651 0.054385 Batch: 1658/4218: 0.030349 0.047735 Batch: 1659/4218: 0.089262 0.111283 Batch: 1660/4218: 0.047685 0.064293 Batch: 1661/4218: 0.045620 0.060338 Batch: 1662/4218: 0.010559 0.029709 Batch: 1663/4218: 0.042082 0.059341 Batch: 1664/4218: 0.037221 0.052959 Batch: 1665/4218: 0.015779 0.029828 Batch: 1666/4218: 0.027162 0.042130 Batch: 1667/4218: 0.037817 0.052965 Batch: 1668/4218: 0.055534 0.072014 Batch: 1669/4218: 0.029591 0.046923 Batch: 1670/4218: 0.013226 0.028254 Batch: 1671/4218: 0.083584 0.105754 Batch: 1672/4218: 0.068511 0.082984 Batch: 1673/4218: 0.011296 0.028246 Batch: 1674/4218: 0.039292 0.053910 Batch: 1675/4218: 0.064987 0.082533 Batch: 1676/4218: 0.053294 0.073171 Batch: 1677/4218: 0.015510 0.030938 Batch: 1678/4218: 0.044062 0.058704 Batch: 1679/4218: 0.036882 0.054413 Batch: 1680/4218: 0.016443 0.033331 Batch: 1681/4218: 0.086697 0.103171 Batch: 1682/4218: 0.068178 0.081613 Batch: 1683/4218: 0.047400 0.062284 Batch: 1684/4218: 0.018624 0.031894 Batch: 1685/4218: 0.021283 0.039240 Batch: 1686/4218: 0.022819 0.048867 Batch: 1687/4218: 0.043090 0.058398 Batch: 1688/4218: 0.030144 0.046214 Batch: 1689/4218: 0.018475 0.035708 Batch: 1690/4218: 0.049390 0.065690 Batch: 1691/4218: 0.056286 0.069722 Batch: 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Batch: 1720/4218: 0.020875 0.035058 Batch: 1721/4218: 0.091985 0.118346 Batch: 1722/4218: 0.071795 0.085787 Batch: 1723/4218: 0.033396 0.052118 Batch: 1724/4218: 0.007502 0.022325 Batch: 1725/4218: 0.025097 0.047699 Batch: 1726/4218: 0.063267 0.079958 Batch: 1727/4218: 0.021804 0.038052 Batch: 1728/4218: 0.021694 0.035693 Batch: 1729/4218: 0.027124 0.041679 Batch: 1730/4218: 0.007705 0.022511 Batch: 1731/4218: 0.043195 0.058102 Batch: 1732/4218: 0.036017 0.049675 Batch: 1733/4218: 0.145212 0.160475 Batch: 1734/4218: 0.030484 0.044531 Batch: 1735/4218: 0.012813 0.031759 Batch: 1736/4218: 0.061039 0.075230 Batch: 1737/4218: 0.035909 0.053121 Batch: 1738/4218: 0.028822 0.051154 Batch: 1739/4218: 0.015505 0.031471 Batch: 1740/4218: 0.024429 0.042728 Batch: 1741/4218: 0.021107 0.035697 Batch: 1742/4218: 0.014406 0.030884 Batch: 1743/4218: 0.011268 0.025197 Batch: 1744/4218: 0.057467 0.071430 Batch: 1745/4218: 0.029555 0.046050 Batch: 1746/4218: 0.061613 0.074343 Batch: 1747/4218: 0.080837 0.095952 Batch: 1748/4218: 0.072142 0.087021 Batch: 1749/4218: 0.051149 0.064660 Batch: 1750/4218: 0.153526 0.166141 Batch: 1751/4218: 0.051185 0.067970 Batch: 1752/4218: 0.066179 0.090804 Batch: 1753/4218: 0.021612 0.034448 Batch: 1754/4218: 0.023915 0.040107 Batch: 1755/4218: 0.026660 0.039982 Batch: 1756/4218: 0.049701 0.065129 Batch: 1757/4218: 0.022143 0.035180 Batch: 1758/4218: 0.027988 0.046816 Batch: 1759/4218: 0.033602 0.047274 Batch: 1760/4218: 0.018445 0.032257 Batch: 1761/4218: 0.023026 0.037854 Batch: 1762/4218: 0.039784 0.054840 Batch: 1763/4218: 0.007413 0.020513 Batch: 1764/4218: 0.054734 0.066786 Batch: 1765/4218: 0.025913 0.039524 Batch: 1766/4218: 0.019339 0.037407 Batch: 1767/4218: 0.011110 0.023153 Batch: 1768/4218: 0.042809 0.055831 Batch: 1769/4218: 0.008486 0.022484 Batch: 1770/4218: 0.114504 0.126095 Batch: 1771/4218: 0.018255 0.030322 Batch: 1772/4218: 0.020150 0.032979 Batch: 1773/4218: 0.026233 0.038636 Batch: 1774/4218: 0.023062 0.037643 Batch: 1775/4218: 0.012206 0.039179 Batch: 1776/4218: 0.013666 0.029473 Batch: 1777/4218: 0.027584 0.040592 Batch: 1778/4218: 0.011645 0.026661 Batch: 1779/4218: 0.026098 0.044882 Batch: 1780/4218: 0.011711 0.046243 Batch: 1781/4218: 0.039472 0.076925 Batch: 1782/4218: 0.024330 0.041524 Batch: 1783/4218: 0.071122 0.083089 Batch: 1784/4218: 0.012454 0.029289 Batch: 1785/4218: 0.033870 0.048957 Batch: 1786/4218: 0.022807 0.036127 Batch: 1787/4218: 0.029584 0.046061 Batch: 1788/4218: 0.010832 0.028355 Batch: 1789/4218: 0.031195 0.049282 Batch: 1790/4218: 0.018205 0.049103 Batch: 1791/4218: 0.006821 0.026335 Batch: 1792/4218: 0.120722 0.145500 Batch: 1793/4218: 0.120960 0.136401 Batch: 1794/4218: 0.020461 0.036235 Batch: 1795/4218: 0.055917 0.071962 Batch: 1796/4218: 0.021126 0.037856 Batch: 1797/4218: 0.027199 0.041472 Batch: 1798/4218: 0.051462 0.064754 Batch: 1799/4218: 0.023140 0.035736 Batch: 1800/4218: 0.015678 0.028325 Batch: 1801/4218: 0.103163 0.116923 Batch: 1802/4218: 0.025513 0.050068 Batch: 1803/4218: 0.014415 0.028309 Batch: 1804/4218: 0.017183 0.032959 Batch: 1805/4218: 0.017875 0.031211 Batch: 1806/4218: 0.020860 0.035142 Batch: 1807/4218: 0.110229 0.123417 Batch: 1808/4218: 0.043062 0.060047 Batch: 1809/4218: 0.018086 0.032038 Batch: 1810/4218: 0.075746 0.096831 Batch: 1811/4218: 0.043389 0.056090 Batch: 1812/4218: 0.026537 0.038974 Batch: 1813/4218: 0.044134 0.056958 Batch: 1814/4218: 0.073439 0.086722 Batch: 1815/4218: 0.013038 0.030091 Batch: 1816/4218: 0.016438 0.035868 Batch: 1817/4218: 0.019424 0.032827 Batch: 1818/4218: 0.052494 0.069867 Batch: 1819/4218: 0.033364 0.047206 Batch: 1820/4218: 0.066843 0.097801 Batch: 1821/4218: 0.035803 0.050039 Batch: 1822/4218: 0.021429 0.035565 Batch: 1823/4218: 0.034250 0.050916 Batch: 1824/4218: 0.102266 0.115387 Batch: 1825/4218: 0.012073 0.026017 Batch: 1826/4218: 0.020253 0.036478 Batch: 1827/4218: 0.061852 0.077845 Batch: 1828/4218: 0.123508 0.137646 Batch: 1829/4218: 0.049454 0.065251 Batch: 1830/4218: 0.029969 0.043448 Batch: 1831/4218: 0.022666 0.035418 Batch: 1832/4218: 0.023357 0.036590 Batch: 1833/4218: 0.055402 0.067216 Batch: 1834/4218: 0.121739 0.134139 Batch: 1835/4218: 0.042985 0.060590 Batch: 1836/4218: 0.019692 0.033917 Batch: 1837/4218: 0.061907 0.075798 Batch: 1838/4218: 0.041138 0.054283 Batch: 1839/4218: 0.025244 0.036967 Batch: 1840/4218: 0.027352 0.043705 Batch: 1841/4218: 0.012320 0.034026 Batch: 1842/4218: 0.024614 0.036631 Batch: 1843/4218: 0.085169 0.097220 Batch: 1844/4218: 0.032290 0.047801 Batch: 1845/4218: 0.089353 0.100710 Batch: 1846/4218: 0.014879 0.026813 Batch: 1847/4218: 0.035507 0.047790 Batch: 1848/4218: 0.017397 0.030017 Batch: 1849/4218: 0.073025 0.084366 Batch: 1850/4218: 0.102869 0.114212 Batch: 1851/4218: 0.022200 0.037567 Batch: 1852/4218: 0.030746 0.042774 Batch: 1853/4218: 0.041670 0.054679 Batch: 1854/4218: 0.012603 0.023574 Batch: 1855/4218: 0.020749 0.031447 Batch: 1856/4218: 0.028680 0.041984 Batch: 1857/4218: 0.034125 0.072539 Batch: 1858/4218: 0.022350 0.034840 Batch: 1859/4218: 0.011889 0.023145 Batch: 1860/4218: 0.032222 0.047190 Batch: 1861/4218: 0.120365 0.134223 Batch: 1862/4218: 0.065268 0.081521 Batch: 1863/4218: 0.058279 0.074365 Batch: 1864/4218: 0.018428 0.030832 Batch: 1865/4218: 0.053355 0.067466 Batch: 1866/4218: 0.009397 0.021256 Batch: 1867/4218: 0.014451 0.027015 Batch: 1868/4218: 0.003736 0.015198 Batch: 1869/4218: 0.018712 0.029498 Batch: 1870/4218: 0.020866 0.032374 Batch: 1871/4218: 0.025204 0.040465 Batch: 1872/4218: 0.012086 0.025382 Batch: 1873/4218: 0.040472 0.053096 Batch: 1874/4218: 0.016312 0.027786 Batch: 1875/4218: 0.043495 0.055090 Batch: 1876/4218: 0.059117 0.072927 Batch: 1877/4218: 0.027479 0.038813 Batch: 1878/4218: 0.079082 0.090471 Batch: 1879/4218: 0.011128 0.024311 Batch: 1880/4218: 0.034339 0.057277 Batch: 1881/4218: 0.113408 0.134674 Batch: 1882/4218: 0.021333 0.044010 Batch: 1883/4218: 0.027634 0.044597 Batch: 1884/4218: 0.016222 0.028615 Batch: 1885/4218: 0.051744 0.064632 Batch: 1886/4218: 0.029828 0.043157 Batch: 1887/4218: 0.004101 0.017556 Batch: 1888/4218: 0.041537 0.053671 Batch: 1889/4218: 0.091311 0.103185 Batch: 1890/4218: 0.155045 0.165943 Batch: 1891/4218: 0.053953 0.065450 Batch: 1892/4218: 0.016595 0.027815 Batch: 1893/4218: 0.058251 0.072525 Batch: 1894/4218: 0.018724 0.029759 Batch: 1895/4218: 0.027789 0.038845 Batch: 1896/4218: 0.011562 0.024528 Batch: 1897/4218: 0.020563 0.030913 Batch: 1898/4218: 0.069893 0.080914 Batch: 1899/4218: 0.023974 0.036484 Batch: 1900/4218: 0.024727 0.034753 Batch: 1901/4218: 0.015369 0.027454 Batch: 1902/4218: 0.029736 0.042456 Batch: 1903/4218: 0.011534 0.021097 Batch: 1904/4218: 0.012182 0.024786 Batch: 1905/4218: 0.018711 0.045377 Batch: 1906/4218: 0.032299 0.047211 Batch: 1907/4218: 0.022256 0.032733 Batch: 1908/4218: 0.012991 0.023262 Batch: 1909/4218: 0.062163 0.073780 Batch: 1910/4218: 0.014965 0.026441 Batch: 1911/4218: 0.033648 0.045237 Batch: 1912/4218: 0.030345 0.041992 Batch: 1913/4218: 0.022100 0.033378 Batch: 1914/4218: 0.013963 0.024491 Batch: 1915/4218: 0.026843 0.039262 Batch: 1916/4218: 0.129331 0.139779 Batch: 1917/4218: 0.007387 0.018360 Batch: 1918/4218: 0.193858 0.210139 Batch: 1919/4218: 0.051310 0.061304 Batch: 1920/4218: 0.049074 0.066121 Batch: 1921/4218: 0.021197 0.034105 Batch: 1922/4218: 0.008158 0.017860 Batch: 1923/4218: 0.026474 0.038426 Batch: 1924/4218: 0.049654 0.060170 Batch: 1925/4218: 0.028507 0.039970 Batch: 1926/4218: 0.044637 0.056635 Batch: 1927/4218: 0.072623 0.082356 Batch: 1928/4218: 0.012088 0.022961 Batch: 1929/4218: 0.101933 0.114808 Batch: 1930/4218: 0.024737 0.034323 Batch: 1931/4218: 0.029659 0.040328 Batch: 1932/4218: 0.014999 0.024527 Batch: 1933/4218: 0.023204 0.032721 Batch: 1934/4218: 0.033686 0.043986 Batch: 1935/4218: 0.037608 0.051923 Batch: 1936/4218: 0.018486 0.030541 Batch: 1937/4218: 0.076630 0.090130 Batch: 1938/4218: 0.050991 0.062053 Batch: 1939/4218: 0.009037 0.023504 Batch: 1940/4218: 0.016979 0.028111 Batch: 1941/4218: 0.034585 0.046186 Batch: 1942/4218: 0.025996 0.039579 Batch: 1943/4218: 0.045633 0.056716 Batch: 1944/4218: 0.036766 0.047920 Batch: 1945/4218: 0.046813 0.061391 Batch: 1946/4218: 0.060913 0.070653 Batch: 1947/4218: 0.016554 0.026814 Batch: 1948/4218: 0.014913 0.024671 Batch: 1949/4218: 0.014637 0.024215 Batch: 1950/4218: 0.037962 0.058302 Batch: 1951/4218: 0.021071 0.033741 Batch: 1952/4218: 0.040112 0.050507 Batch: 1953/4218: 0.021278 0.032066 Batch: 1954/4218: 0.129129 0.138560 Batch: 1955/4218: 0.027449 0.041384 Batch: 1956/4218: 0.043469 0.054092 Batch: 1957/4218: 0.045608 0.059172 Batch: 1958/4218: 0.084303 0.103007 Batch: 1959/4218: 0.028430 0.039950 Batch: 1960/4218: 0.099693 0.112414 Batch: 1961/4218: 0.245579 0.255571 Batch: 1962/4218: 0.034263 0.045873 Batch: 1963/4218: 0.028565 0.039995 Batch: 1964/4218: 0.043501 0.055239 Batch: 1965/4218: 0.006486 0.017843 Batch: 1966/4218: 0.029743 0.043727 Batch: 1967/4218: 0.029119 0.040697 Batch: 1968/4218: 0.019310 0.028646 Batch: 1969/4218: 0.045796 0.055662 Batch: 1970/4218: 0.101093 0.112603 Batch: 1971/4218: 0.018282 0.034087 Batch: 1972/4218: 0.030384 0.042967 Batch: 1973/4218: 0.061777 0.072139 Batch: 1974/4218: 0.017443 0.030417 Batch: 1975/4218: 0.031830 0.042548 Batch: 1976/4218: 0.025735 0.036316 Batch: 1977/4218: 0.017039 0.027440 Batch: 1978/4218: 0.124076 0.133602 Batch: 1979/4218: 0.069593 0.080067 Batch: 1980/4218: 0.012548 0.021726 Batch: 1981/4218: 0.083587 0.093587 Batch: 1982/4218: 0.014993 0.024497 Batch: 1983/4218: 0.007535 0.018237 Batch: 1984/4218: 0.018470 0.028469 Batch: 1985/4218: 0.042369 0.051372 Batch: 1986/4218: 0.154888 0.170095 Batch: 1987/4218: 0.022675 0.034038 Batch: 1988/4218: 0.042684 0.054718 Batch: 1989/4218: 0.031656 0.046080 Batch: 1990/4218: 0.023228 0.034146 Batch: 1991/4218: 0.009696 0.020253 Batch: 1992/4218: 0.014467 0.023455 Batch: 1993/4218: 0.024364 0.034934 Batch: 1994/4218: 0.008189 0.018655 Batch: 1995/4218: 0.030375 0.042330 Batch: 1996/4218: 0.030712 0.043110 Batch: 1997/4218: 0.049809 0.058987 Batch: 1998/4218: 0.027965 0.038527 Batch: 1999/4218: 0.012761 0.021541 Batch: 2000/4218: 0.046262 0.055580 Batch: 2001/4218: 0.017107 0.028273 Batch: 2002/4218: 0.042671 0.052301 Batch: 2003/4218: 0.022281 0.035962 Batch: 2004/4218: 0.022151 0.032975 Batch: 2005/4218: 0.049133 0.060264 Batch: 2006/4218: 0.016224 0.027052 Batch: 2007/4218: 0.100279 0.109300 Batch: 2008/4218: 0.029235 0.037979 Batch: 2009/4218: 0.081207 0.090775 Batch: 2010/4218: 0.013537 0.023104 Batch: 2011/4218: 0.055210 0.064702 Batch: 2012/4218: 0.015598 0.025760 Batch: 2013/4218: 0.031009 0.041669 Batch: 2014/4218: 0.022185 0.032077 Batch: 2015/4218: 0.014007 0.029900 Batch: 2016/4218: 0.032226 0.042042 Batch: 2017/4218: 0.028185 0.037025 Batch: 2018/4218: 0.044285 0.055142 Batch: 2019/4218: 0.010861 0.024298 Batch: 2020/4218: 0.112456 0.121557 Batch: 2021/4218: 0.026289 0.038560 Batch: 2022/4218: 0.010020 0.023616 Batch: 2023/4218: 0.026662 0.041770 Batch: 2024/4218: 0.012665 0.020822 Batch: 2025/4218: 0.012775 0.024056 Batch: 2026/4218: 0.027839 0.038048 Batch: 2027/4218: 0.177677 0.187928 Batch: 2028/4218: 0.017602 0.025863 Batch: 2029/4218: 0.016215 0.026236 Batch: 2030/4218: 0.025551 0.035897 Batch: 2031/4218: 0.014657 0.025541 Batch: 2032/4218: 0.010793 0.020870 Batch: 2033/4218: 0.020783 0.035390 Batch: 2034/4218: 0.011249 0.021472 Batch: 2035/4218: 0.067877 0.077239 Batch: 2036/4218: 0.016622 0.028797 Batch: 2037/4218: 0.016266 0.028639 Batch: 2038/4218: 0.094968 0.109137 Batch: 2039/4218: 0.039480 0.051130 Batch: 2040/4218: 0.017163 0.026923 Batch: 2041/4218: 0.039917 0.051484 Batch: 2042/4218: 0.020080 0.030675 Batch: 2043/4218: 0.012664 0.026203 Batch: 2044/4218: 0.015797 0.024834 Batch: 2045/4218: 0.057627 0.066103 Batch: 2046/4218: 0.049387 0.057598 Batch: 2047/4218: 0.007809 0.017790 Batch: 2048/4218: 0.021258 0.030901 Batch: 2049/4218: 0.040061 0.050644 Batch: 2050/4218: 0.014982 0.026246 Batch: 2051/4218: 0.013553 0.021873 Batch: 2052/4218: 0.022078 0.031045 Batch: 2053/4218: 0.008183 0.016332 Batch: 2054/4218: 0.023941 0.033944 Batch: 2055/4218: 0.035187 0.043720 Batch: 2056/4218: 0.017238 0.026665 Batch: 2057/4218: 0.050823 0.060806 Batch: 2058/4218: 0.031963 0.041815 Batch: 2059/4218: 0.105178 0.113641 Batch: 2060/4218: 0.010746 0.020725 Batch: 2061/4218: 0.012244 0.030436 Batch: 2062/4218: 0.046458 0.055458 Batch: 2063/4218: 0.107858 0.118077 Batch: 2064/4218: 0.034060 0.042219 Batch: 2065/4218: 0.026491 0.035624 Batch: 2066/4218: 0.040364 0.051901 Batch: 2067/4218: 0.045319 0.054357 Batch: 2068/4218: 0.007527 0.018078 Batch: 2069/4218: 0.058616 0.068077 Batch: 2070/4218: 0.044539 0.055905 Batch: 2071/4218: 0.040393 0.049277 Batch: 2072/4218: 0.046463 0.055404 Batch: 2073/4218: 0.036030 0.044912 Batch: 2074/4218: 0.107805 0.119491 Batch: 2075/4218: 0.035693 0.044066 Batch: 2076/4218: 0.019042 0.030233 Batch: 2077/4218: 0.060598 0.070552 Batch: 2078/4218: 0.026646 0.036585 Batch: 2079/4218: 0.174877 0.187642 Batch: 2080/4218: 0.012684 0.021935 Batch: 2081/4218: 0.023282 0.031595 Batch: 2082/4218: 0.034680 0.044542 Batch: 2083/4218: 0.124681 0.132967 Batch: 2084/4218: 0.033381 0.043719 Batch: 2085/4218: 0.051207 0.060541 Batch: 2086/4218: 0.044472 0.053227 Batch: 2087/4218: 0.028290 0.036042 Batch: 2088/4218: 0.063380 0.072610 Batch: 2089/4218: 0.043705 0.051943 Batch: 2090/4218: 0.047153 0.055666 Batch: 2091/4218: 0.102260 0.110302 Batch: 2092/4218: 0.047514 0.056735 Batch: 2093/4218: 0.110518 0.118189 Batch: 2094/4218: 0.062535 0.070586 Batch: 2095/4218: 0.029361 0.039734 Batch: 2096/4218: 0.025429 0.033866 Batch: 2097/4218: 0.142268 0.149839 Batch: 2098/4218: 0.076769 0.084634 Batch: 2099/4218: 0.022736 0.031143 Batch: 2100/4218: 0.018077 0.029164 Batch: 2101/4218: 0.015796 0.023978 Batch: 2102/4218: 0.023978 0.031906 Batch: 2103/4218: 0.034094 0.041965 Batch: 2104/4218: 0.026252 0.036954 Batch: 2105/4218: 0.027636 0.035783 Batch: 2106/4218: 0.055791 0.064471 Batch: 2107/4218: 0.024573 0.032679 Batch: 2108/4218: 0.029484 0.037017 Batch: 2109/4218: 0.014784 0.024247 Batch: 2110/4218: 0.019830 0.031344 Batch: 2111/4218: 0.019342 0.027965 Batch: 2112/4218: 0.015899 0.027096 Batch: 2113/4218: 0.012411 0.022253 Batch: 2114/4218: 0.022635 0.030469 Batch: 2115/4218: 0.026992 0.034774 Batch: 2116/4218: 0.076966 0.088492 Batch: 2117/4218: 0.040545 0.051473 Batch: 2118/4218: 0.013239 0.022876 Batch: 2119/4218: 0.016235 0.024294 Batch: 2120/4218: 0.015246 0.024634 Batch: 2121/4218: 0.116547 0.126654 Batch: 2122/4218: 0.007089 0.014833 Batch: 2123/4218: 0.038411 0.046254 Batch: 2124/4218: 0.035239 0.043258 Batch: 2125/4218: 0.011922 0.023037 Batch: 2126/4218: 0.034441 0.042988 Batch: 2127/4218: 0.037531 0.045892 Batch: 2128/4218: 0.015273 0.026967 Batch: 2129/4218: 0.129714 0.137051 Batch: 2130/4218: 0.019176 0.027623 Batch: 2131/4218: 0.035690 0.044573 Batch: 2132/4218: 0.016224 0.023631 Batch: 2133/4218: 0.102180 0.110279 Batch: 2134/4218: 0.059142 0.067441 Batch: 2135/4218: 0.081275 0.088810 Batch: 2136/4218: 0.026866 0.034989 Batch: 2137/4218: 0.025428 0.032596 Batch: 2138/4218: 0.011178 0.018390 Batch: 2139/4218: 0.025096 0.051687 Batch: 2140/4218: 0.027937 0.035631 Batch: 2141/4218: 0.020890 0.030011 Batch: 2142/4218: 0.044566 0.053018 Batch: 2143/4218: 0.011840 0.021314 Batch: 2144/4218: 0.030370 0.038955 Batch: 2145/4218: 0.020005 0.028788 Batch: 2146/4218: 0.108214 0.116119 Batch: 2147/4218: 0.030090 0.038454 Batch: 2148/4218: 0.030063 0.037696 Batch: 2149/4218: 0.034595 0.041865 Batch: 2150/4218: 0.011176 0.018567 Batch: 2151/4218: 0.044899 0.051957 Batch: 2152/4218: 0.047517 0.055104 Batch: 2153/4218: 0.025225 0.036839 Batch: 2154/4218: 0.053732 0.060407 Batch: 2155/4218: 0.033465 0.046933 Batch: 2156/4218: 0.026083 0.033910 Batch: 2157/4218: 0.150362 0.158459 Batch: 2158/4218: 0.015536 0.023704 Batch: 2159/4218: 0.049123 0.061881 Batch: 2160/4218: 0.019447 0.027012 Batch: 2161/4218: 0.023867 0.033165 Batch: 2162/4218: 0.012988 0.023048 Batch: 2163/4218: 0.098289 0.106694 Batch: 2164/4218: 0.017417 0.029344 Batch: 2165/4218: 0.029642 0.038104 Batch: 2166/4218: 0.011717 0.019669 Batch: 2167/4218: 0.032692 0.043558 Batch: 2168/4218: 0.028512 0.035894 Batch: 2169/4218: 0.015445 0.023048 Batch: 2170/4218: 0.038816 0.046248 Batch: 2171/4218: 0.017527 0.027887 Batch: 2172/4218: 0.024202 0.032790 Batch: 2173/4218: 0.033118 0.040733 Batch: 2174/4218: 0.020265 0.028410 Batch: 2175/4218: 0.042689 0.050077 Batch: 2176/4218: 0.046371 0.053406 Batch: 2177/4218: 0.016846 0.024221 Batch: 2178/4218: 0.119597 0.127383 Batch: 2179/4218: 0.051334 0.062128 Batch: 2180/4218: 0.019608 0.027020 Batch: 2181/4218: 0.039507 0.050879 Batch: 2182/4218: 0.024084 0.034758 Batch: 2183/4218: 0.016789 0.026370 Batch: 2184/4218: 0.013884 0.020815 Batch: 2185/4218: 0.074662 0.081660 Batch: 2186/4218: 0.025163 0.032131 Batch: 2187/4218: 0.029797 0.037163 Batch: 2188/4218: 0.263341 0.273313 Batch: 2189/4218: 0.028367 0.035227 Batch: 2190/4218: 0.038572 0.047386 Batch: 2191/4218: 0.023684 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Batch: 2275/4218: 0.013949 0.028167 Batch: 2276/4218: 0.032351 0.047595 Batch: 2277/4218: 0.024753 0.036801 Batch: 2278/4218: 0.019827 0.030975 Batch: 2279/4218: 0.049548 0.057880 Batch: 2280/4218: 0.019315 0.026961 Batch: 2281/4218: 0.023555 0.031748 Batch: 2282/4218: 0.055619 0.063011 Batch: 2283/4218: 0.010718 0.022446 Batch: 2284/4218: 0.021518 0.030624 Batch: 2285/4218: 0.048822 0.055619 Batch: 2286/4218: 0.084843 0.091414 Batch: 2287/4218: 0.025906 0.033232 Batch: 2288/4218: 0.054424 0.061742 Batch: 2289/4218: 0.040377 0.049390 Batch: 2290/4218: 0.008629 0.016405 Batch: 2291/4218: 0.053236 0.060774 Batch: 2292/4218: 0.018349 0.025854 Batch: 2293/4218: 0.012960 0.019894 Batch: 2294/4218: 0.039270 0.046633 Batch: 2295/4218: 0.018214 0.026129 Batch: 2296/4218: 0.011360 0.018099 Batch: 2297/4218: 0.027483 0.034346 Batch: 2298/4218: 0.053346 0.061647 Batch: 2299/4218: 0.015460 0.024698 Batch: 2300/4218: 0.091057 0.098011 Batch: 2301/4218: 0.084846 0.096325 Batch: 2302/4218: 0.014151 0.020957 Batch: 2303/4218: 0.012748 0.023507 Batch: 2304/4218: 0.019880 0.026412 Batch: 2305/4218: 0.019489 0.026372 Batch: 2306/4218: 0.012978 0.019672 Batch: 2307/4218: 0.028898 0.035541 Batch: 2308/4218: 0.027035 0.034672 Batch: 2309/4218: 0.126751 0.136130 Batch: 2310/4218: 0.010310 0.017366 Batch: 2311/4218: 0.028569 0.034855 Batch: 2312/4218: 0.036606 0.045398 Batch: 2313/4218: 0.038014 0.044757 Batch: 2314/4218: 0.048630 0.055471 Batch: 2315/4218: 0.084590 0.097509 Batch: 2316/4218: 0.028947 0.035491 Batch: 2317/4218: 0.024354 0.033692 Batch: 2318/4218: 0.021293 0.028053 Batch: 2319/4218: 0.012756 0.020577 Batch: 2320/4218: 0.034384 0.041599 Batch: 2321/4218: 0.006651 0.014235 Batch: 2322/4218: 0.057074 0.063979 Batch: 2323/4218: 0.016709 0.025118 Batch: 2324/4218: 0.023120 0.030351 Batch: 2325/4218: 0.010941 0.017983 Batch: 2326/4218: 0.017798 0.025225 Batch: 2327/4218: 0.031742 0.041205 Batch: 2328/4218: 0.042758 0.052077 Batch: 2329/4218: 0.057132 0.063465 Batch: 2330/4218: 0.008488 0.015097 Batch: 2331/4218: 0.024206 0.031233 Batch: 2332/4218: 0.015941 0.022775 Batch: 2333/4218: 0.020763 0.027048 Batch: 2334/4218: 0.028047 0.034060 Batch: 2335/4218: 0.023461 0.030692 Batch: 2336/4218: 0.061134 0.069342 Batch: 2337/4218: 0.038505 0.044476 Batch: 2338/4218: 0.024236 0.031377 Batch: 2339/4218: 0.112069 0.117942 Batch: 2340/4218: 0.062796 0.070346 Batch: 2341/4218: 0.061691 0.069792 Batch: 2342/4218: 0.077951 0.085058 Batch: 2343/4218: 0.185952 0.192152 Batch: 2344/4218: 0.063280 0.069934 Batch: 2345/4218: 0.026790 0.032980 Batch: 2346/4218: 0.013622 0.020805 Batch: 2347/4218: 0.051265 0.057892 Batch: 2348/4218: 0.029826 0.035805 Batch: 2349/4218: 0.037561 0.045742 Batch: 2350/4218: 0.027672 0.033708 Batch: 2351/4218: 0.022725 0.031239 Batch: 2352/4218: 0.025453 0.032106 Batch: 2353/4218: 0.049679 0.058357 Batch: 2354/4218: 0.031220 0.037021 Batch: 2355/4218: 0.029697 0.035590 Batch: 2356/4218: 0.015943 0.021849 Batch: 2357/4218: 0.023410 0.029365 Batch: 2358/4218: 0.010548 0.016938 Batch: 2359/4218: 0.023472 0.029379 Batch: 2360/4218: 0.047276 0.055397 Batch: 2361/4218: 0.036821 0.043109 Batch: 2362/4218: 0.054274 0.060675 Batch: 2363/4218: 0.038191 0.044195 Batch: 2364/4218: 0.037089 0.043703 Batch: 2365/4218: 0.027064 0.033134 Batch: 2366/4218: 0.051174 0.058238 Batch: 2367/4218: 0.060388 0.071256 Batch: 2368/4218: 0.022417 0.028894 Batch: 2369/4218: 0.041293 0.048530 Batch: 2370/4218: 0.008731 0.015306 Batch: 2371/4218: 0.026754 0.034437 Batch: 2372/4218: 0.026151 0.033996 Batch: 2373/4218: 0.056761 0.062260 Batch: 2374/4218: 0.087284 0.094122 Batch: 2375/4218: 0.009925 0.016637 Batch: 2376/4218: 0.036602 0.043574 Batch: 2377/4218: 0.153740 0.159776 Batch: 2378/4218: 0.070764 0.076820 Batch: 2379/4218: 0.056044 0.062654 Batch: 2380/4218: 0.059370 0.065248 Batch: 2381/4218: 0.044773 0.050756 Batch: 2382/4218: 0.047354 0.053561 Batch: 2383/4218: 0.130609 0.136290 Batch: 2384/4218: 0.051787 0.057675 Batch: 2385/4218: 0.047220 0.052519 Batch: 2386/4218: 0.042920 0.048161 Batch: 2387/4218: 0.043230 0.050243 Batch: 2388/4218: 0.011127 0.017156 Batch: 2389/4218: 0.040304 0.048034 Batch: 2390/4218: 0.043145 0.049164 Batch: 2391/4218: 0.035935 0.042216 Batch: 2392/4218: 0.021223 0.026722 Batch: 2393/4218: 0.054981 0.063534 Batch: 2394/4218: 0.021055 0.026952 Batch: 2395/4218: 0.009237 0.015941 Batch: 2396/4218: 0.047865 0.053935 Batch: 2397/4218: 0.039140 0.044785 Batch: 2398/4218: 0.011470 0.016693 Batch: 2399/4218: 0.017784 0.023848 Batch: 2400/4218: 0.022406 0.028944 Batch: 2401/4218: 0.072554 0.079301 Batch: 2402/4218: 0.012097 0.018709 Batch: 2403/4218: 0.017830 0.023013 Batch: 2404/4218: 0.018679 0.025437 Batch: 2405/4218: 0.021190 0.027187 Batch: 2406/4218: 0.011787 0.018431 Batch: 2407/4218: 0.012846 0.018790 Batch: 2408/4218: 0.028123 0.033928 Batch: 2409/4218: 0.040868 0.046377 Batch: 2410/4218: 0.024161 0.032817 Batch: 2411/4218: 0.012663 0.018065 Batch: 2412/4218: 0.022486 0.028631 Batch: 2413/4218: 0.022138 0.027446 Batch: 2414/4218: 0.079765 0.084994 Batch: 2415/4218: 0.031649 0.038059 Batch: 2416/4218: 0.067765 0.073534 Batch: 2417/4218: 0.011976 0.017141 Batch: 2418/4218: 0.011685 0.020939 Batch: 2419/4218: 0.013959 0.019990 Batch: 2420/4218: 0.056301 0.061616 Batch: 2421/4218: 0.009526 0.014702 Batch: 2422/4218: 0.009861 0.015278 Batch: 2423/4218: 0.059790 0.065591 Batch: 2424/4218: 0.172433 0.177798 Batch: 2425/4218: 0.036059 0.041915 Batch: 2426/4218: 0.039009 0.044860 Batch: 2427/4218: 0.150186 0.157646 Batch: 2428/4218: 0.024318 0.029692 Batch: 2429/4218: 0.020871 0.028149 Batch: 2430/4218: 0.048284 0.077323 Batch: 2431/4218: 0.018542 0.025496 Batch: 2432/4218: 0.036341 0.042676 Batch: 2433/4218: 0.030569 0.036099 Batch: 2434/4218: 0.016652 0.023072 Batch: 2435/4218: 0.010114 0.017100 Batch: 2436/4218: 0.027731 0.033444 Batch: 2437/4218: 0.183707 0.190311 Batch: 2438/4218: 0.034789 0.041503 Batch: 2439/4218: 0.074768 0.083184 Batch: 2440/4218: 0.018753 0.024493 Batch: 2441/4218: 0.012546 0.020621 Batch: 2442/4218: 0.008011 0.013503 Batch: 2443/4218: 0.025195 0.031319 Batch: 2444/4218: 0.018098 0.023437 Batch: 2445/4218: 0.061199 0.068764 Batch: 2446/4218: 0.011890 0.017026 Batch: 2447/4218: 0.018833 0.024086 Batch: 2448/4218: 0.012119 0.017721 Batch: 2449/4218: 0.129601 0.135862 Batch: 2450/4218: 0.014582 0.019868 Batch: 2451/4218: 0.113445 0.121238 Batch: 2452/4218: 0.041461 0.046749 Batch: 2453/4218: 0.024889 0.030244 Batch: 2454/4218: 0.052870 0.058712 Batch: 2455/4218: 0.023529 0.029079 Batch: 2456/4218: 0.031796 0.037899 Batch: 2457/4218: 0.037003 0.043050 Batch: 2458/4218: 0.033654 0.039761 Batch: 2459/4218: 0.011560 0.018107 Batch: 2460/4218: 0.057287 0.062608 Batch: 2461/4218: 0.047914 0.053568 Batch: 2462/4218: 0.021030 0.025929 Batch: 2463/4218: 0.009000 0.016704 Batch: 2464/4218: 0.029353 0.034497 Batch: 2465/4218: 0.021016 0.027039 Batch: 2466/4218: 0.046754 0.052544 Batch: 2467/4218: 0.023171 0.033559 Batch: 2468/4218: 0.022809 0.028010 Batch: 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Batch: 2497/4218: 0.034585 0.041785 Batch: 2498/4218: 0.018339 0.026060 Batch: 2499/4218: 0.062357 0.067433 Batch: 2500/4218: 0.029273 0.035325 Batch: 2501/4218: 0.020424 0.026178 Batch: 2502/4218: 0.028567 0.034730 Batch: 2503/4218: 0.014867 0.022461 Batch: 2504/4218: 0.023734 0.029126 Batch: 2505/4218: 0.016168 0.021589 Batch: 2506/4218: 0.173536 0.179566 Batch: 2507/4218: 0.037239 0.043143 Batch: 2508/4218: 0.040884 0.045905 Batch: 2509/4218: 0.032780 0.038180 Batch: 2510/4218: 0.037931 0.045093 Batch: 2511/4218: 0.021807 0.030614 Batch: 2512/4218: 0.021243 0.026696 Batch: 2513/4218: 0.030344 0.035702 Batch: 2514/4218: 0.242716 0.247714 Batch: 2515/4218: 0.068042 0.074565 Batch: 2516/4218: 0.013702 0.020465 Batch: 2517/4218: 0.030874 0.037718 Batch: 2518/4218: 0.026425 0.031974 Batch: 2519/4218: 0.137647 0.143820 Batch: 2520/4218: 0.081899 0.087641 Batch: 2521/4218: 0.038195 0.043302 Batch: 2522/4218: 0.020576 0.027144 Batch: 2523/4218: 0.020388 0.026406 Batch: 2524/4218: 0.019895 0.024462 Batch: 2525/4218: 0.046619 0.054224 Batch: 2526/4218: 0.025991 0.031668 Batch: 2527/4218: 0.051985 0.061423 Batch: 2528/4218: 0.108952 0.115584 Batch: 2529/4218: 0.032211 0.038537 Batch: 2530/4218: 0.008470 0.015791 Batch: 2531/4218: 0.026652 0.033725 Batch: 2532/4218: 0.028926 0.033693 Batch: 2533/4218: 0.019770 0.026255 Batch: 2534/4218: 0.027619 0.033426 Batch: 2535/4218: 0.039942 0.047561 Batch: 2536/4218: 0.038854 0.044739 Batch: 2537/4218: 0.020640 0.026075 Batch: 2538/4218: 0.021313 0.027012 Batch: 2539/4218: 0.032844 0.038264 Batch: 2540/4218: 0.012004 0.021004 Batch: 2541/4218: 0.023881 0.029772 Batch: 2542/4218: 0.023728 0.030981 Batch: 2543/4218: 0.029316 0.034696 Batch: 2544/4218: 0.082835 0.090936 Batch: 2545/4218: 0.026858 0.033311 Batch: 2546/4218: 0.028117 0.039336 Batch: 2547/4218: 0.009393 0.014458 Batch: 2548/4218: 0.035417 0.042383 Batch: 2549/4218: 0.027074 0.032841 Batch: 2550/4218: 0.011451 0.021505 Batch: 2551/4218: 0.014556 0.019671 Batch: 2552/4218: 0.010140 0.016346 Batch: 2553/4218: 0.037021 0.042978 Batch: 2554/4218: 0.010783 0.015788 Batch: 2555/4218: 0.028969 0.036720 Batch: 2556/4218: 0.012051 0.017404 Batch: 2557/4218: 0.017118 0.021897 Batch: 2558/4218: 0.039994 0.048060 Batch: 2559/4218: 0.019066 0.026047 Batch: 2560/4218: 0.048038 0.053544 Batch: 2561/4218: 0.056861 0.062780 Batch: 2562/4218: 0.107131 0.114292 Batch: 2563/4218: 0.051294 0.056339 Batch: 2564/4218: 0.015462 0.020615 Batch: 2565/4218: 0.010389 0.016209 Batch: 2566/4218: 0.039057 0.043667 Batch: 2567/4218: 0.015884 0.022919 Batch: 2568/4218: 0.019819 0.025641 Batch: 2569/4218: 0.013541 0.018102 Batch: 2570/4218: 0.020010 0.025360 Batch: 2571/4218: 0.019304 0.024618 Batch: 2572/4218: 0.016895 0.021656 Batch: 2573/4218: 0.018985 0.024748 Batch: 2574/4218: 0.015013 0.020991 Batch: 2575/4218: 0.087812 0.092788 Batch: 2576/4218: 0.017506 0.022123 Batch: 2577/4218: 0.017509 0.022348 Batch: 2578/4218: 0.041157 0.045681 Batch: 2579/4218: 0.016127 0.020807 Batch: 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Batch: 2608/4218: 0.033750 0.041224 Batch: 2609/4218: 0.010358 0.015314 Batch: 2610/4218: 0.027872 0.033879 Batch: 2611/4218: 0.022584 0.031372 Batch: 2612/4218: 0.010570 0.015428 Batch: 2613/4218: 0.009016 0.018233 Batch: 2614/4218: 0.026271 0.031381 Batch: 2615/4218: 0.015406 0.020075 Batch: 2616/4218: 0.058385 0.063931 Batch: 2617/4218: 0.029986 0.034785 Batch: 2618/4218: 0.069702 0.074219 Batch: 2619/4218: 0.087789 0.092508 Batch: 2620/4218: 0.031635 0.035963 Batch: 2621/4218: 0.009358 0.014017 Batch: 2622/4218: 0.012726 0.017383 Batch: 2623/4218: 0.017138 0.021882 Batch: 2624/4218: 0.017379 0.022483 Batch: 2625/4218: 0.035411 0.040286 Batch: 2626/4218: 0.010192 0.015692 Batch: 2627/4218: 0.067863 0.072686 Batch: 2628/4218: 0.044639 0.049614 Batch: 2629/4218: 0.022897 0.027680 Batch: 2630/4218: 0.091209 0.095495 Batch: 2631/4218: 0.054024 0.059395 Batch: 2632/4218: 0.030014 0.034165 Batch: 2633/4218: 0.014972 0.019822 Batch: 2634/4218: 0.021939 0.026998 Batch: 2635/4218: 0.008710 0.014075 Batch: 2636/4218: 0.049622 0.057931 Batch: 2637/4218: 0.013578 0.018190 Batch: 2638/4218: 0.015598 0.019915 Batch: 2639/4218: 0.056990 0.061231 Batch: 2640/4218: 0.009693 0.014416 Batch: 2641/4218: 0.037563 0.042024 Batch: 2642/4218: 0.033438 0.038162 Batch: 2643/4218: 0.014630 0.020385 Batch: 2644/4218: 0.011257 0.015368 Batch: 2645/4218: 0.016878 0.021954 Batch: 2646/4218: 0.039980 0.046100 Batch: 2647/4218: 0.013351 0.017543 Batch: 2648/4218: 0.036509 0.041072 Batch: 2649/4218: 0.018008 0.022314 Batch: 2650/4218: 0.013737 0.017948 Batch: 2651/4218: 0.043721 0.048193 Batch: 2652/4218: 0.013579 0.018500 Batch: 2653/4218: 0.012693 0.017514 Batch: 2654/4218: 0.034536 0.038708 Batch: 2655/4218: 0.045035 0.049292 Batch: 2656/4218: 0.048457 0.052588 Batch: 2657/4218: 0.021512 0.026265 Batch: 2658/4218: 0.040351 0.045875 Batch: 2659/4218: 0.071334 0.075349 Batch: 2660/4218: 0.055773 0.061481 Batch: 2661/4218: 0.030745 0.035466 Batch: 2662/4218: 0.018242 0.022528 Batch: 2663/4218: 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Batch: 2719/4218: 0.011883 0.015859 Batch: 2720/4218: 0.059491 0.064611 Batch: 2721/4218: 0.035308 0.039947 Batch: 2722/4218: 0.041479 0.047156 Batch: 2723/4218: 0.010273 0.014206 Batch: 2724/4218: 0.052234 0.056175 Batch: 2725/4218: 0.032365 0.036449 Batch: 2726/4218: 0.058580 0.063930 Batch: 2727/4218: 0.011118 0.014777 Batch: 2728/4218: 0.014765 0.020300 Batch: 2729/4218: 0.034745 0.039425 Batch: 2730/4218: 0.075097 0.079038 Batch: 2731/4218: 0.006271 0.012479 Batch: 2732/4218: 0.016267 0.020105 Batch: 2733/4218: 0.011192 0.015189 Batch: 2734/4218: 0.023820 0.029429 Batch: 2735/4218: 0.023388 0.027541 Batch: 2736/4218: 0.099160 0.103391 Batch: 2737/4218: 0.066963 0.070743 Batch: 2738/4218: 0.009731 0.013464 Batch: 2739/4218: 0.028245 0.034234 Batch: 2740/4218: 0.028976 0.032821 Batch: 2741/4218: 0.015032 0.019606 Batch: 2742/4218: 0.031894 0.035822 Batch: 2743/4218: 0.011772 0.016368 Batch: 2744/4218: 0.025448 0.029908 Batch: 2745/4218: 0.017689 0.022007 Batch: 2746/4218: 0.083746 0.087471 Batch: 2747/4218: 0.006067 0.011596 Batch: 2748/4218: 0.010093 0.014909 Batch: 2749/4218: 0.010477 0.014505 Batch: 2750/4218: 0.022545 0.027119 Batch: 2751/4218: 0.022293 0.026813 Batch: 2752/4218: 0.012937 0.016750 Batch: 2753/4218: 0.026989 0.032272 Batch: 2754/4218: 0.019679 0.027630 Batch: 2755/4218: 0.027900 0.033306 Batch: 2756/4218: 0.039968 0.043558 Batch: 2757/4218: 0.022515 0.026369 Batch: 2758/4218: 0.038285 0.042103 Batch: 2759/4218: 0.035190 0.040234 Batch: 2760/4218: 0.020074 0.025035 Batch: 2761/4218: 0.031930 0.037120 Batch: 2762/4218: 0.014274 0.018372 Batch: 2763/4218: 0.022126 0.026354 Batch: 2764/4218: 0.021857 0.025713 Batch: 2765/4218: 0.022001 0.025885 Batch: 2766/4218: 0.046789 0.051794 Batch: 2767/4218: 0.033624 0.037562 Batch: 2768/4218: 0.041292 0.045347 Batch: 2769/4218: 0.019786 0.024925 Batch: 2770/4218: 0.028364 0.032068 Batch: 2771/4218: 0.027031 0.032120 Batch: 2772/4218: 0.066252 0.070394 Batch: 2773/4218: 0.028993 0.033087 Batch: 2774/4218: 0.007686 0.011327 Batch: 2775/4218: 0.005389 0.009260 Batch: 2776/4218: 0.016896 0.020918 Batch: 2777/4218: 0.030534 0.034093 Batch: 2778/4218: 0.011542 0.015335 Batch: 2779/4218: 0.030120 0.033930 Batch: 2780/4218: 0.009442 0.013096 Batch: 2781/4218: 0.007227 0.011270 Batch: 2782/4218: 0.011970 0.015643 Batch: 2783/4218: 0.012496 0.017964 Batch: 2784/4218: 0.046665 0.050248 Batch: 2785/4218: 0.024293 0.028611 Batch: 2786/4218: 0.043447 0.050123 Batch: 2787/4218: 0.072669 0.078334 Batch: 2788/4218: 0.009992 0.013803 Batch: 2789/4218: 0.014656 0.018403 Batch: 2790/4218: 0.013442 0.018548 Batch: 2791/4218: 0.026509 0.030190 Batch: 2792/4218: 0.015631 0.020919 Batch: 2793/4218: 0.044749 0.048901 Batch: 2794/4218: 0.127803 0.132190 Batch: 2795/4218: 0.120295 0.124278 Batch: 2796/4218: 0.006259 0.010083 Batch: 2797/4218: 0.014555 0.018358 Batch: 2798/4218: 0.011485 0.014922 Batch: 2799/4218: 0.035760 0.040644 Batch: 2800/4218: 0.025942 0.029570 Batch: 2801/4218: 0.020673 0.024658 Batch: 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Batch: 2830/4218: 0.072091 0.077207 Batch: 2831/4218: 0.059077 0.065086 Batch: 2832/4218: 0.022918 0.027372 Batch: 2833/4218: 0.026484 0.030045 Batch: 2834/4218: 0.042079 0.046997 Batch: 2835/4218: 0.036719 0.046671 Batch: 2836/4218: 0.049536 0.053882 Batch: 2837/4218: 0.046038 0.056589 Batch: 2838/4218: 0.015231 0.020016 Batch: 2839/4218: 0.041636 0.046729 Batch: 2840/4218: 0.068377 0.072967 Batch: 2841/4218: 0.025780 0.030088 Batch: 2842/4218: 0.198759 0.205553 Batch: 2843/4218: 0.018046 0.029585 Batch: 2844/4218: 0.011521 0.015751 Batch: 2845/4218: 0.022063 0.029862 Batch: 2846/4218: 0.077788 0.082510 Batch: 2847/4218: 0.013077 0.017572 Batch: 2848/4218: 0.023466 0.027061 Batch: 2849/4218: 0.006540 0.010386 Batch: 2850/4218: 0.028345 0.033886 Batch: 2851/4218: 0.032904 0.039359 Batch: 2852/4218: 0.023898 0.028453 Batch: 2853/4218: 0.025649 0.030630 Batch: 2854/4218: 0.010924 0.015442 Batch: 2855/4218: 0.006504 0.011485 Batch: 2856/4218: 0.050475 0.054547 Batch: 2857/4218: 0.022628 0.027835 Batch: 2858/4218: 0.018179 0.021917 Batch: 2859/4218: 0.013886 0.018047 Batch: 2860/4218: 0.033339 0.037694 Batch: 2861/4218: 0.011830 0.016797 Batch: 2862/4218: 0.055181 0.059518 Batch: 2863/4218: 0.093767 0.099586 Batch: 2864/4218: 0.137787 0.145624 Batch: 2865/4218: 0.033129 0.039078 Batch: 2866/4218: 0.020969 0.025243 Batch: 2867/4218: 0.015973 0.021564 Batch: 2868/4218: 0.018788 0.022993 Batch: 2869/4218: 0.022932 0.028747 Batch: 2870/4218: 0.008484 0.013272 Batch: 2871/4218: 0.072492 0.076949 Batch: 2872/4218: 0.023110 0.027446 Batch: 2873/4218: 0.006823 0.011589 Batch: 2874/4218: 0.028076 0.032582 Batch: 2875/4218: 0.026861 0.031621 Batch: 2876/4218: 0.027294 0.037439 Batch: 2877/4218: 0.022272 0.025831 Batch: 2878/4218: 0.010705 0.014454 Batch: 2879/4218: 0.009053 0.012899 Batch: 2880/4218: 0.015115 0.022718 Batch: 2881/4218: 0.015782 0.021700 Batch: 2882/4218: 0.029461 0.037672 Batch: 2883/4218: 0.044758 0.049290 Batch: 2884/4218: 0.083277 0.088807 Batch: 2885/4218: 0.010704 0.014630 Batch: 2886/4218: 0.042554 0.046876 Batch: 2887/4218: 0.036804 0.040735 Batch: 2888/4218: 0.019775 0.023928 Batch: 2889/4218: 0.020104 0.027630 Batch: 2890/4218: 0.028544 0.035403 Batch: 2891/4218: 0.038456 0.043598 Batch: 2892/4218: 0.020064 0.025346 Batch: 2893/4218: 0.033544 0.037327 Batch: 2894/4218: 0.072217 0.079084 Batch: 2895/4218: 0.018848 0.024520 Batch: 2896/4218: 0.048372 0.052116 Batch: 2897/4218: 0.019786 0.023385 Batch: 2898/4218: 0.030935 0.035874 Batch: 2899/4218: 0.059021 0.063296 Batch: 2900/4218: 0.013363 0.017044 Batch: 2901/4218: 0.014215 0.017874 Batch: 2902/4218: 0.032811 0.051777 Batch: 2903/4218: 0.093068 0.098318 Batch: 2904/4218: 0.083033 0.087133 Batch: 2905/4218: 0.013080 0.019251 Batch: 2906/4218: 0.039467 0.045303 Batch: 2907/4218: 0.013905 0.021686 Batch: 2908/4218: 0.043840 0.052244 Batch: 2909/4218: 0.040879 0.047609 Batch: 2910/4218: 0.027284 0.036307 Batch: 2911/4218: 0.023787 0.031283 Batch: 2912/4218: 0.028122 0.033890 Batch: 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Batch: 2941/4218: 0.017424 0.026202 Batch: 2942/4218: 0.026676 0.031331 Batch: 2943/4218: 0.037924 0.042073 Batch: 2944/4218: 0.055792 0.059997 Batch: 2945/4218: 0.031158 0.035602 Batch: 2946/4218: 0.073048 0.077433 Batch: 2947/4218: 0.026222 0.037279 Batch: 2948/4218: 0.051070 0.055358 Batch: 2949/4218: 0.035365 0.040359 Batch: 2950/4218: 0.068762 0.073020 Batch: 2951/4218: 0.008874 0.013834 Batch: 2952/4218: 0.035079 0.038800 Batch: 2953/4218: 0.026446 0.030719 Batch: 2954/4218: 0.018281 0.022211 Batch: 2955/4218: 0.014253 0.018293 Batch: 2956/4218: 0.048503 0.052160 Batch: 2957/4218: 0.015713 0.020064 Batch: 2958/4218: 0.023747 0.027619 Batch: 2959/4218: 0.010905 0.015995 Batch: 2960/4218: 0.025858 0.030595 Batch: 2961/4218: 0.017319 0.021370 Batch: 2962/4218: 0.067868 0.073146 Batch: 2963/4218: 0.098030 0.101786 Batch: 2964/4218: 0.053725 0.057969 Batch: 2965/4218: 0.021643 0.026971 Batch: 2966/4218: 0.027413 0.031251 Batch: 2967/4218: 0.007993 0.011942 Batch: 2968/4218: 0.036839 0.040544 Batch: 2969/4218: 0.040875 0.044775 Batch: 2970/4218: 0.040883 0.044644 Batch: 2971/4218: 0.017115 0.021189 Batch: 2972/4218: 0.028346 0.032315 Batch: 2973/4218: 0.011449 0.017762 Batch: 2974/4218: 0.018456 0.022021 Batch: 2975/4218: 0.022873 0.027415 Batch: 2976/4218: 0.038168 0.042926 Batch: 2977/4218: 0.016798 0.021328 Batch: 2978/4218: 0.014829 0.018727 Batch: 2979/4218: 0.016352 0.020459 Batch: 2980/4218: 0.005374 0.008893 Batch: 2981/4218: 0.027561 0.030931 Batch: 2982/4218: 0.020340 0.024499 Batch: 2983/4218: 0.023928 0.027362 Batch: 2984/4218: 0.091474 0.095299 Batch: 2985/4218: 0.016986 0.020733 Batch: 2986/4218: 0.032737 0.036297 Batch: 2987/4218: 0.014135 0.018039 Batch: 2988/4218: 0.019777 0.024086 Batch: 2989/4218: 0.021006 0.024761 Batch: 2990/4218: 0.028969 0.032464 Batch: 2991/4218: 0.027303 0.030799 Batch: 2992/4218: 0.032438 0.036102 Batch: 2993/4218: 0.019164 0.022517 Batch: 2994/4218: 0.083188 0.086414 Batch: 2995/4218: 0.098408 0.101963 Batch: 2996/4218: 0.060435 0.064328 Batch: 2997/4218: 0.021080 0.024948 Batch: 2998/4218: 0.018439 0.024508 Batch: 2999/4218: 0.032818 0.036667 Batch: 3000/4218: 0.022289 0.026101 Batch: 3001/4218: 0.037252 0.042285 Batch: 3002/4218: 0.040915 0.044694 Batch: 3003/4218: 0.020314 0.024605 Batch: 3004/4218: 0.044631 0.048445 Batch: 3005/4218: 0.021706 0.025281 Batch: 3006/4218: 0.010219 0.013545 Batch: 3007/4218: 0.016641 0.020149 Batch: 3008/4218: 0.025344 0.028898 Batch: 3009/4218: 0.015407 0.018753 Batch: 3010/4218: 0.033741 0.037024 Batch: 3011/4218: 0.039858 0.043270 Batch: 3012/4218: 0.018232 0.021790 Batch: 3013/4218: 0.074698 0.078037 Batch: 3014/4218: 0.062204 0.065886 Batch: 3015/4218: 0.029677 0.033309 Batch: 3016/4218: 0.045282 0.048610 Batch: 3017/4218: 0.049084 0.052825 Batch: 3018/4218: 0.065867 0.069397 Batch: 3019/4218: 0.015028 0.020164 Batch: 3020/4218: 0.030016 0.033450 Batch: 3021/4218: 0.038065 0.042782 Batch: 3022/4218: 0.053748 0.056941 Batch: 3023/4218: 0.043205 0.046332 Batch: 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Batch: 3052/4218: 0.017546 0.020698 Batch: 3053/4218: 0.024100 0.028583 Batch: 3054/4218: 0.047894 0.050944 Batch: 3055/4218: 0.009353 0.013058 Batch: 3056/4218: 0.043671 0.046993 Batch: 3057/4218: 0.004234 0.007412 Batch: 3058/4218: 0.028160 0.032290 Batch: 3059/4218: 0.042587 0.046332 Batch: 3060/4218: 0.044133 0.047859 Batch: 3061/4218: 0.051195 0.054456 Batch: 3062/4218: 0.011363 0.014824 Batch: 3063/4218: 0.015009 0.018084 Batch: 3064/4218: 0.066981 0.070267 Batch: 3065/4218: 0.024795 0.027997 Batch: 3066/4218: 0.090084 0.097445 Batch: 3067/4218: 0.043094 0.046006 Batch: 3068/4218: 0.019521 0.022407 Batch: 3069/4218: 0.018351 0.022288 Batch: 3070/4218: 0.029753 0.033885 Batch: 3071/4218: 0.070055 0.072985 Batch: 3072/4218: 0.033948 0.036928 Batch: 3073/4218: 0.032465 0.035276 Batch: 3074/4218: 0.036699 0.039770 Batch: 3075/4218: 0.012470 0.015436 Batch: 3076/4218: 0.012121 0.015210 Batch: 3077/4218: 0.006452 0.010075 Batch: 3078/4218: 0.008888 0.013516 Batch: 3079/4218: 0.041073 0.044558 Batch: 3080/4218: 0.108228 0.110996 Batch: 3081/4218: 0.024172 0.027066 Batch: 3082/4218: 0.021241 0.024259 Batch: 3083/4218: 0.009488 0.012754 Batch: 3084/4218: 0.013612 0.016599 Batch: 3085/4218: 0.055389 0.059055 Batch: 3086/4218: 0.032547 0.036947 Batch: 3087/4218: 0.025186 0.028071 Batch: 3088/4218: 0.009454 0.012353 Batch: 3089/4218: 0.011399 0.015029 Batch: 3090/4218: 0.040236 0.043332 Batch: 3091/4218: 0.017267 0.020941 Batch: 3092/4218: 0.022772 0.025997 Batch: 3093/4218: 0.024887 0.027858 Batch: 3094/4218: 0.019810 0.023006 Batch: 3095/4218: 0.145423 0.151261 Batch: 3096/4218: 0.038143 0.042100 Batch: 3097/4218: 0.029788 0.034215 Batch: 3098/4218: 0.033669 0.037406 Batch: 3099/4218: 0.024170 0.027578 Batch: 3100/4218: 0.030387 0.033151 Batch: 3101/4218: 0.034377 0.037294 Batch: 3102/4218: 0.016689 0.019900 Batch: 3103/4218: 0.010512 0.014081 Batch: 3104/4218: 0.082761 0.086098 Batch: 3105/4218: 0.027174 0.029983 Batch: 3106/4218: 0.028243 0.031156 Batch: 3107/4218: 0.047681 0.050579 Batch: 3108/4218: 0.010313 0.013507 Batch: 3109/4218: 0.025572 0.030826 Batch: 3110/4218: 0.070792 0.073715 Batch: 3111/4218: 0.013980 0.016761 Batch: 3112/4218: 0.017991 0.020874 Batch: 3113/4218: 0.051290 0.054673 Batch: 3114/4218: 0.018745 0.021553 Batch: 3115/4218: 0.034452 0.037916 Batch: 3116/4218: 0.144590 0.148208 Batch: 3117/4218: 0.039401 0.042265 Batch: 3118/4218: 0.005773 0.008442 Batch: 3119/4218: 0.034484 0.038196 Batch: 3120/4218: 0.026242 0.029391 Batch: 3121/4218: 0.013226 0.015945 Batch: 3122/4218: 0.030001 0.032848 Batch: 3123/4218: 0.038942 0.041998 Batch: 3124/4218: 0.009621 0.012221 Batch: 3125/4218: 0.021573 0.024529 Batch: 3126/4218: 0.030213 0.032956 Batch: 3127/4218: 0.055680 0.059561 Batch: 3128/4218: 0.008495 0.011074 Batch: 3129/4218: 0.022045 0.025070 Batch: 3130/4218: 0.022698 0.025450 Batch: 3131/4218: 0.050450 0.053500 Batch: 3132/4218: 0.042834 0.046050 Batch: 3133/4218: 0.050051 0.052734 Batch: 3134/4218: 0.103909 0.108544 Batch: 3135/4218: 0.018438 0.021432 Batch: 3136/4218: 0.069606 0.072203 Batch: 3137/4218: 0.059895 0.063067 Batch: 3138/4218: 0.023440 0.027362 Batch: 3139/4218: 0.014976 0.024347 Batch: 3140/4218: 0.026990 0.032473 Batch: 3141/4218: 0.099542 0.102933 Batch: 3142/4218: 0.017301 0.020155 Batch: 3143/4218: 0.104776 0.107594 Batch: 3144/4218: 0.053994 0.057441 Batch: 3145/4218: 0.037597 0.040415 Batch: 3146/4218: 0.058042 0.061173 Batch: 3147/4218: 0.027065 0.030564 Batch: 3148/4218: 0.063191 0.066122 Batch: 3149/4218: 0.022776 0.025651 Batch: 3150/4218: 0.078428 0.081536 Batch: 3151/4218: 0.015939 0.018891 Batch: 3152/4218: 0.023047 0.026095 Batch: 3153/4218: 0.029557 0.032757 Batch: 3154/4218: 0.071373 0.074051 Batch: 3155/4218: 0.025580 0.028938 Batch: 3156/4218: 0.052873 0.055971 Batch: 3157/4218: 0.019753 0.023878 Batch: 3158/4218: 0.065056 0.067871 Batch: 3159/4218: 0.033378 0.036250 Batch: 3160/4218: 0.015637 0.018296 Batch: 3161/4218: 0.031427 0.037045 Batch: 3162/4218: 0.057509 0.060272 Batch: 3163/4218: 0.041466 0.044666 Batch: 3164/4218: 0.026020 0.028493 Batch: 3165/4218: 0.011213 0.013912 Batch: 3166/4218: 0.030994 0.033886 Batch: 3167/4218: 0.056857 0.059670 Batch: 3168/4218: 0.010614 0.016176 Batch: 3169/4218: 0.014691 0.021691 Batch: 3170/4218: 0.062971 0.065761 Batch: 3171/4218: 0.104738 0.107892 Batch: 3172/4218: 0.037637 0.041017 Batch: 3173/4218: 0.032104 0.036219 Batch: 3174/4218: 0.052834 0.055421 Batch: 3175/4218: 0.023267 0.027089 Batch: 3176/4218: 0.021516 0.024389 Batch: 3177/4218: 0.081197 0.084002 Batch: 3178/4218: 0.064181 0.068260 Batch: 3179/4218: 0.014943 0.018833 Batch: 3180/4218: 0.022222 0.025345 Batch: 3181/4218: 0.026226 0.029103 Batch: 3182/4218: 0.043539 0.046237 Batch: 3183/4218: 0.029813 0.033144 Batch: 3184/4218: 0.067452 0.070799 Batch: 3185/4218: 0.056241 0.059124 Batch: 3186/4218: 0.023133 0.026218 Batch: 3187/4218: 0.036009 0.039897 Batch: 3188/4218: 0.022248 0.025811 Batch: 3189/4218: 0.017547 0.021967 Batch: 3190/4218: 0.021173 0.027268 Batch: 3191/4218: 0.055965 0.059579 Batch: 3192/4218: 0.069356 0.072180 Batch: 3193/4218: 0.013916 0.016327 Batch: 3194/4218: 0.054263 0.057481 Batch: 3195/4218: 0.023371 0.026316 Batch: 3196/4218: 0.021080 0.024491 Batch: 3197/4218: 0.020505 0.023453 Batch: 3198/4218: 0.043074 0.045722 Batch: 3199/4218: 0.011865 0.014421 Batch: 3200/4218: 0.025722 0.028649 Batch: 3201/4218: 0.043538 0.046266 Batch: 3202/4218: 0.015169 0.019221 Batch: 3203/4218: 0.033692 0.036807 Batch: 3204/4218: 0.080997 0.083634 Batch: 3205/4218: 0.100286 0.102833 Batch: 3206/4218: 0.046474 0.051078 Batch: 3207/4218: 0.024917 0.027427 Batch: 3208/4218: 0.051691 0.054979 Batch: 3209/4218: 0.080735 0.083639 Batch: 3210/4218: 0.030361 0.033165 Batch: 3211/4218: 0.039812 0.042430 Batch: 3212/4218: 0.129173 0.131692 Batch: 3213/4218: 0.030499 0.033039 Batch: 3214/4218: 0.084203 0.087137 Batch: 3215/4218: 0.053800 0.056388 Batch: 3216/4218: 0.010521 0.015087 Batch: 3217/4218: 0.020882 0.023580 Batch: 3218/4218: 0.017859 0.020604 Batch: 3219/4218: 0.032035 0.034800 Batch: 3220/4218: 0.030676 0.033615 Batch: 3221/4218: 0.020511 0.023024 Batch: 3222/4218: 0.022156 0.024794 Batch: 3223/4218: 0.016640 0.019679 Batch: 3224/4218: 0.038899 0.041901 Batch: 3225/4218: 0.122020 0.127189 Batch: 3226/4218: 0.027874 0.031669 Batch: 3227/4218: 0.027893 0.030191 Batch: 3228/4218: 0.014578 0.019229 Batch: 3229/4218: 0.113696 0.118696 Batch: 3230/4218: 0.022824 0.025581 Batch: 3231/4218: 0.033307 0.035824 Batch: 3232/4218: 0.134897 0.142414 Batch: 3233/4218: 0.019350 0.022805 Batch: 3234/4218: 0.007706 0.010218 Batch: 3235/4218: 0.010400 0.014243 Batch: 3236/4218: 0.094308 0.096802 Batch: 3237/4218: 0.009302 0.012195 Batch: 3238/4218: 0.012445 0.014932 Batch: 3239/4218: 0.024551 0.027343 Batch: 3240/4218: 0.032415 0.036680 Batch: 3241/4218: 0.018471 0.021408 Batch: 3242/4218: 0.060802 0.064051 Batch: 3243/4218: 0.020230 0.023868 Batch: 3244/4218: 0.034426 0.037256 Batch: 3245/4218: 0.056300 0.059140 Batch: 3246/4218: 0.097227 0.099911 Batch: 3247/4218: 0.018807 0.021400 Batch: 3248/4218: 0.037241 0.040367 Batch: 3249/4218: 0.023693 0.026163 Batch: 3250/4218: 0.056128 0.058587 Batch: 3251/4218: 0.024963 0.028160 Batch: 3252/4218: 0.009797 0.012773 Batch: 3253/4218: 0.009319 0.014096 Batch: 3254/4218: 0.008799 0.011933 Batch: 3255/4218: 0.050518 0.053170 Batch: 3256/4218: 0.025909 0.028858 Batch: 3257/4218: 0.032734 0.035493 Batch: 3258/4218: 0.069827 0.072969 Batch: 3259/4218: 0.017518 0.021361 Batch: 3260/4218: 0.020115 0.022467 Batch: 3261/4218: 0.015395 0.018276 Batch: 3262/4218: 0.068183 0.072860 Batch: 3263/4218: 0.028171 0.031977 Batch: 3264/4218: 0.034806 0.038003 Batch: 3265/4218: 0.005872 0.008371 Batch: 3266/4218: 0.009717 0.012254 Batch: 3267/4218: 0.016043 0.018533 Batch: 3268/4218: 0.118201 0.121271 Batch: 3269/4218: 0.023620 0.026999 Batch: 3270/4218: 0.079826 0.082600 Batch: 3271/4218: 0.010385 0.013976 Batch: 3272/4218: 0.015877 0.018302 Batch: 3273/4218: 0.091317 0.095242 Batch: 3274/4218: 0.077321 0.079638 Batch: 3275/4218: 0.027499 0.030048 Batch: 3276/4218: 0.020866 0.023992 Batch: 3277/4218: 0.052150 0.054647 Batch: 3278/4218: 0.011042 0.014543 Batch: 3279/4218: 0.075994 0.078488 Batch: 3280/4218: 0.027641 0.030637 Batch: 3281/4218: 0.024313 0.029279 Batch: 3282/4218: 0.014964 0.017558 Batch: 3283/4218: 0.044008 0.046815 Batch: 3284/4218: 0.036945 0.039193 Batch: 3285/4218: 0.033345 0.035807 Batch: 3286/4218: 0.059868 0.062763 Batch: 3287/4218: 0.054798 0.057018 Batch: 3288/4218: 0.032373 0.036703 Batch: 3289/4218: 0.007909 0.010642 Batch: 3290/4218: 0.010068 0.012888 Batch: 3291/4218: 0.068016 0.071512 Batch: 3292/4218: 0.059450 0.062176 Batch: 3293/4218: 0.023708 0.026046 Batch: 3294/4218: 0.025422 0.027737 Batch: 3295/4218: 0.041089 0.043271 Batch: 3296/4218: 0.024881 0.027400 Batch: 3297/4218: 0.030898 0.034735 Batch: 3298/4218: 0.013453 0.016086 Batch: 3299/4218: 0.037452 0.040206 Batch: 3300/4218: 0.013756 0.019448 Batch: 3301/4218: 0.007190 0.010447 Batch: 3302/4218: 0.022920 0.027591 Batch: 3303/4218: 0.028514 0.032593 Batch: 3304/4218: 0.010643 0.013939 Batch: 3305/4218: 0.037957 0.040468 Batch: 3306/4218: 0.033182 0.035568 Batch: 3307/4218: 0.007754 0.010156 Batch: 3308/4218: 0.004845 0.008320 Batch: 3309/4218: 0.089662 0.092057 Batch: 3310/4218: 0.014947 0.017335 Batch: 3311/4218: 0.011963 0.014257 Batch: 3312/4218: 0.006538 0.008902 Batch: 3313/4218: 0.004372 0.007334 Batch: 3314/4218: 0.018611 0.020949 Batch: 3315/4218: 0.013537 0.015733 Batch: 3316/4218: 0.061037 0.066030 Batch: 3317/4218: 0.014882 0.018553 Batch: 3318/4218: 0.044636 0.046857 Batch: 3319/4218: 0.010515 0.013144 Batch: 3320/4218: 0.011811 0.014727 Batch: 3321/4218: 0.025276 0.029587 Batch: 3322/4218: 0.012366 0.014546 Batch: 3323/4218: 0.012198 0.014777 Batch: 3324/4218: 0.043119 0.049582 Batch: 3325/4218: 0.120476 0.123126 Batch: 3326/4218: 0.155675 0.157984 Batch: 3327/4218: 0.026081 0.028754 Batch: 3328/4218: 0.057614 0.060039 Batch: 3329/4218: 0.038409 0.040783 Batch: 3330/4218: 0.023548 0.026049 Batch: 3331/4218: 0.023111 0.025753 Batch: 3332/4218: 0.014767 0.020443 Batch: 3333/4218: 0.017258 0.019450 Batch: 3334/4218: 0.064755 0.067472 Batch: 3335/4218: 0.014808 0.017074 Batch: 3336/4218: 0.057363 0.059870 Batch: 3337/4218: 0.027656 0.029863 Batch: 3338/4218: 0.017031 0.019474 Batch: 3339/4218: 0.014056 0.016271 Batch: 3340/4218: 0.021432 0.023608 Batch: 3341/4218: 0.019527 0.022196 Batch: 3342/4218: 0.053322 0.057334 Batch: 3343/4218: 0.030464 0.033456 Batch: 3344/4218: 0.114916 0.118349 Batch: 3345/4218: 0.019789 0.022006 Batch: 3346/4218: 0.086513 0.089872 Batch: 3347/4218: 0.018422 0.020597 Batch: 3348/4218: 0.019319 0.021588 Batch: 3349/4218: 0.024191 0.026503 Batch: 3350/4218: 0.009064 0.011236 Batch: 3351/4218: 0.010067 0.012371 Batch: 3352/4218: 0.007526 0.010614 Batch: 3353/4218: 0.049942 0.053622 Batch: 3354/4218: 0.053310 0.056131 Batch: 3355/4218: 0.005959 0.009176 Batch: 3356/4218: 0.025994 0.028446 Batch: 3357/4218: 0.052709 0.055105 Batch: 3358/4218: 0.009945 0.014277 Batch: 3359/4218: 0.031636 0.033955 Batch: 3360/4218: 0.030280 0.032999 Batch: 3361/4218: 0.030533 0.032734 Batch: 3362/4218: 0.109965 0.112141 Batch: 3363/4218: 0.050501 0.053240 Batch: 3364/4218: 0.028602 0.033220 Batch: 3365/4218: 0.078606 0.080764 Batch: 3366/4218: 0.047982 0.050094 Batch: 3367/4218: 0.033808 0.036168 Batch: 3368/4218: 0.029465 0.031646 Batch: 3369/4218: 0.019135 0.021960 Batch: 3370/4218: 0.029262 0.031516 Batch: 3371/4218: 0.014744 0.017436 Batch: 3372/4218: 0.052410 0.055031 Batch: 3373/4218: 0.011768 0.013884 Batch: 3374/4218: 0.027200 0.029484 Batch: 3375/4218: 0.095464 0.097675 Batch: 3376/4218: 0.014452 0.019531 Batch: 3377/4218: 0.015331 0.019292 Batch: 3378/4218: 0.043404 0.045801 Batch: 3379/4218: 0.024128 0.028612 Batch: 3380/4218: 0.025833 0.030673 Batch: 3381/4218: 0.052968 0.055122 Batch: 3382/4218: 0.017564 0.020019 Batch: 3383/4218: 0.013523 0.015826 Batch: 3384/4218: 0.036398 0.040027 Batch: 3385/4218: 0.019483 0.021794 Batch: 3386/4218: 0.051388 0.053867 Batch: 3387/4218: 0.042540 0.044945 Batch: 3388/4218: 0.138959 0.141611 Batch: 3389/4218: 0.026845 0.029520 Batch: 3390/4218: 0.045993 0.048389 Batch: 3391/4218: 0.007720 0.009836 Batch: 3392/4218: 0.056375 0.058705 Batch: 3393/4218: 0.016691 0.019143 Batch: 3394/4218: 0.012587 0.014868 Batch: 3395/4218: 0.018880 0.021003 Batch: 3396/4218: 0.070167 0.072745 Batch: 3397/4218: 0.055326 0.057642 Batch: 3398/4218: 0.028934 0.030960 Batch: 3399/4218: 0.113680 0.117140 Batch: 3400/4218: 0.012393 0.014873 Batch: 3401/4218: 0.014774 0.017802 Batch: 3402/4218: 0.039985 0.042332 Batch: 3403/4218: 0.021170 0.023192 Batch: 3404/4218: 0.037499 0.039665 Batch: 3405/4218: 0.004139 0.006451 Batch: 3406/4218: 0.012200 0.014386 Batch: 3407/4218: 0.034964 0.037752 Batch: 3408/4218: 0.013659 0.016001 Batch: 3409/4218: 0.010022 0.012181 Batch: 3410/4218: 0.019546 0.021725 Batch: 3411/4218: 0.011121 0.014031 Batch: 3412/4218: 0.032055 0.036173 Batch: 3413/4218: 0.021480 0.024041 Batch: 3414/4218: 0.022854 0.026869 Batch: 3415/4218: 0.011671 0.014326 Batch: 3416/4218: 0.032015 0.034052 Batch: 3417/4218: 0.063222 0.068165 Batch: 3418/4218: 0.045616 0.049060 Batch: 3419/4218: 0.009761 0.012426 Batch: 3420/4218: 0.030230 0.033557 Batch: 3421/4218: 0.023477 0.031869 Batch: 3422/4218: 0.015716 0.017939 Batch: 3423/4218: 0.044675 0.047708 Batch: 3424/4218: 0.019557 0.021615 Batch: 3425/4218: 0.044260 0.066343 Batch: 3426/4218: 0.027549 0.030250 Batch: 3427/4218: 0.033675 0.036949 Batch: 3428/4218: 0.041114 0.045064 Batch: 3429/4218: 0.018381 0.024356 Batch: 3430/4218: 0.027283 0.029402 Batch: 3431/4218: 0.044944 0.047385 Batch: 3432/4218: 0.018167 0.020325 Batch: 3433/4218: 0.025608 0.032388 Batch: 3434/4218: 0.048939 0.053356 Batch: 3435/4218: 0.022780 0.027200 Batch: 3436/4218: 0.053283 0.056440 Batch: 3437/4218: 0.015944 0.021927 Batch: 3438/4218: 0.047612 0.050659 Batch: 3439/4218: 0.011584 0.030609 Batch: 3440/4218: 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Batch: 3496/4218: 0.041053 0.044225 Batch: 3497/4218: 0.033905 0.037095 Batch: 3498/4218: 0.012709 0.015222 Batch: 3499/4218: 0.022984 0.030700 Batch: 3500/4218: 0.028307 0.031066 Batch: 3501/4218: 0.034042 0.038012 Batch: 3502/4218: 0.011960 0.015074 Batch: 3503/4218: 0.040268 0.046371 Batch: 3504/4218: 0.028468 0.031456 Batch: 3505/4218: 0.023683 0.026260 Batch: 3506/4218: 0.014786 0.018166 Batch: 3507/4218: 0.040877 0.043822 Batch: 3508/4218: 0.017446 0.020731 Batch: 3509/4218: 0.027660 0.030347 Batch: 3510/4218: 0.086419 0.089181 Batch: 3511/4218: 0.092496 0.095169 Batch: 3512/4218: 0.033437 0.038677 Batch: 3513/4218: 0.012611 0.020016 Batch: 3514/4218: 0.019915 0.022810 Batch: 3515/4218: 0.019445 0.025240 Batch: 3516/4218: 0.024273 0.027205 Batch: 3517/4218: 0.068671 0.071347 Batch: 3518/4218: 0.024983 0.027708 Batch: 3519/4218: 0.015853 0.018829 Batch: 3520/4218: 0.019136 0.021805 Batch: 3521/4218: 0.009994 0.012649 Batch: 3522/4218: 0.023175 0.025741 Batch: 3523/4218: 0.085695 0.094028 Batch: 3524/4218: 0.022931 0.031466 Batch: 3525/4218: 0.023818 0.027278 Batch: 3526/4218: 0.018457 0.021008 Batch: 3527/4218: 0.030316 0.033933 Batch: 3528/4218: 0.038078 0.041020 Batch: 3529/4218: 0.006276 0.009959 Batch: 3530/4218: 0.017812 0.020838 Batch: 3531/4218: 0.004969 0.009708 Batch: 3532/4218: 0.077179 0.080073 Batch: 3533/4218: 0.048264 0.051350 Batch: 3534/4218: 0.029427 0.032601 Batch: 3535/4218: 0.012133 0.017026 Batch: 3536/4218: 0.032490 0.035321 Batch: 3537/4218: 0.095815 0.098436 Batch: 3538/4218: 0.029694 0.032278 Batch: 3539/4218: 0.018172 0.021041 Batch: 3540/4218: 0.017361 0.020257 Batch: 3541/4218: 0.015002 0.017781 Batch: 3542/4218: 0.057500 0.061051 Batch: 3543/4218: 0.105424 0.110426 Batch: 3544/4218: 0.108378 0.110786 Batch: 3545/4218: 0.057109 0.060034 Batch: 3546/4218: 0.045748 0.048485 Batch: 3547/4218: 0.046929 0.049556 Batch: 3548/4218: 0.038393 0.041362 Batch: 3549/4218: 0.028221 0.040939 Batch: 3550/4218: 0.020128 0.023121 Batch: 3551/4218: 0.022386 0.160848 Batch: 3552/4218: 0.017099 0.020280 Batch: 3553/4218: 0.022690 0.025443 Batch: 3554/4218: 0.028418 0.033699 Batch: 3555/4218: 0.007962 0.011398 Batch: 3556/4218: 0.085962 0.089055 Batch: 3557/4218: 0.019130 0.069295 Batch: 3558/4218: 0.037104 0.041307 Batch: 3559/4218: 0.031221 0.277971 Batch: 3560/4218: 0.027454 0.778131 Batch: 3561/4218: 0.013033 0.016157 Batch: 3562/4218: 0.048669 0.053986 Batch: 3563/4218: 0.077393 0.278094 Batch: 3564/4218: 0.023775 0.030299 Batch: 3565/4218: 0.045313 0.051783 Batch: 3566/4218: 0.028072 0.033661 Batch: 3567/4218: 0.012089 0.016485 Batch: 3568/4218: 0.235207 0.243754 Batch: 3569/4218: 0.011348 0.016228 Batch: 3570/4218: 0.033412 0.594966 Batch: 3571/4218: 0.008433 0.013739 Batch: 3572/4218: 0.022169 0.342873 Batch: 3573/4218: 0.048754 0.548939 Batch: 3574/4218: 0.034030 0.044072 Batch: 3575/4218: 0.016268 0.023292 Batch: 3576/4218: 0.013166 0.020175 Batch: 3577/4218: 0.054017 0.153324 Batch: 3578/4218: 0.009304 0.023174 Batch: 3579/4218: 0.018934 0.056692 Batch: 3580/4218: 0.071972 0.079666 Batch: 3581/4218: 0.047551 0.061155 Batch: 3582/4218: 0.009113 0.019269 Batch: 3583/4218: 0.005720 0.015011 Batch: 3584/4218: 0.019232 0.027950 Batch: 3585/4218: 0.033112 0.048737 Batch: 3586/4218: 0.019486 0.147608 Batch: 3587/4218: 0.039722 0.054423 Batch: 3588/4218: 0.050890 0.081594 Batch: 3589/4218: 0.053288 0.077115 Batch: 3590/4218: 0.214417 0.255706 Batch: 3591/4218: 0.027658 0.036168 Batch: 3592/4218: 0.023447 0.031882 Batch: 3593/4218: 0.053734 0.065879 Batch: 3594/4218: 0.040052 0.049057 Batch: 3595/4218: 0.009728 0.018925 Batch: 3596/4218: 0.078547 0.088093 Batch: 3597/4218: 0.037833 0.047474 Batch: 3598/4218: 0.031490 0.040719 Batch: 3599/4218: 0.021298 0.034650 Batch: 3600/4218: 0.020354 0.029766 Batch: 3601/4218: 0.024348 0.033397 Batch: 3602/4218: 0.039222 0.049707 Batch: 3603/4218: 0.012630 0.023333 Batch: 3604/4218: 0.041822 0.053294 Batch: 3605/4218: 0.023139 0.036835 Batch: 3606/4218: 0.018423 0.482294 Batch: 3607/4218: 0.019901 0.029967 Batch: 3608/4218: 0.011644 0.020748 Batch: 3609/4218: 0.015406 0.024801 Batch: 3610/4218: 0.010604 0.020061 Batch: 3611/4218: 0.028622 0.038432 Batch: 3612/4218: 0.015069 0.187507 Batch: 3613/4218: 0.070407 0.080583 Batch: 3614/4218: 0.073860 0.083348 Batch: 3615/4218: 0.010479 0.020052 Batch: 3616/4218: 0.029979 0.039421 Batch: 3617/4218: 0.017724 0.027145 Batch: 3618/4218: 0.026589 0.036186 Batch: 3619/4218: 0.020120 0.056634 Batch: 3620/4218: 0.054413 0.063842 Batch: 3621/4218: 0.027489 0.037119 Batch: 3622/4218: 0.028700 0.038541 Batch: 3623/4218: 0.020846 0.030987 Batch: 3624/4218: 0.013819 0.023797 Batch: 3625/4218: 0.040177 0.049967 Batch: 3626/4218: 0.012499 0.022076 Batch: 3627/4218: 0.092979 0.102250 Batch: 3628/4218: 0.025482 0.035015 Batch: 3629/4218: 0.050733 0.061229 Batch: 3630/4218: 0.011444 0.020425 Batch: 3631/4218: 0.058214 0.067929 Batch: 3632/4218: 0.080185 0.090363 Batch: 3633/4218: 0.020617 0.030630 Batch: 3634/4218: 0.050995 0.060249 Batch: 3635/4218: 0.029315 0.037990 Batch: 3636/4218: 0.093405 0.102007 Batch: 3637/4218: 0.081645 0.090578 Batch: 3638/4218: 0.090203 0.102741 Batch: 3639/4218: 0.010680 0.020571 Batch: 3640/4218: 0.020371 0.028982 Batch: 3641/4218: 0.017220 0.025454 Batch: 3642/4218: 0.011704 0.024257 Batch: 3643/4218: 0.014006 0.022740 Batch: 3644/4218: 0.013754 0.021711 Batch: 3645/4218: 0.009189 0.017286 Batch: 3646/4218: 0.014660 0.023216 Batch: 3647/4218: 0.058217 0.068491 Batch: 3648/4218: 0.011463 0.021398 Batch: 3649/4218: 0.006761 0.014282 Batch: 3650/4218: 0.015060 0.023186 Batch: 3651/4218: 0.025914 0.033371 Batch: 3652/4218: 0.021728 0.029346 Batch: 3653/4218: 0.022579 0.029877 Batch: 3654/4218: 0.020358 0.027665 Batch: 3655/4218: 0.042823 0.050188 Batch: 3656/4218: 0.025289 0.032354 Batch: 3657/4218: 0.023109 0.030663 Batch: 3658/4218: 0.026614 0.034040 Batch: 3659/4218: 0.033986 0.041052 Batch: 3660/4218: 0.045337 0.054013 Batch: 3661/4218: 0.016738 0.023744 Batch: 3662/4218: 0.034313 0.044417 Batch: 3663/4218: 0.038856 0.045575 Batch: 3664/4218: 0.026437 0.033109 Batch: 3665/4218: 0.051278 0.059387 Batch: 3666/4218: 0.056038 0.065032 Batch: 3667/4218: 0.051683 0.061907 Batch: 3668/4218: 0.009455 0.015901 Batch: 3669/4218: 0.017894 0.044434 Batch: 3670/4218: 0.029409 0.036012 Batch: 3671/4218: 0.089041 0.097752 Batch: 3672/4218: 0.014623 0.020803 Batch: 3673/4218: 0.017810 0.024101 Batch: 3674/4218: 0.066523 0.083550 Batch: 3675/4218: 0.037091 0.043346 Batch: 3676/4218: 0.029046 0.035899 Batch: 3677/4218: 0.025815 0.032486 Batch: 3678/4218: 0.038818 0.045299 Batch: 3679/4218: 0.050524 0.056641 Batch: 3680/4218: 0.041304 0.047398 Batch: 3681/4218: 0.027199 0.036097 Batch: 3682/4218: 0.013043 0.020256 Batch: 3683/4218: 0.050253 0.057008 Batch: 3684/4218: 0.084742 0.091241 Batch: 3685/4218: 0.032765 0.038773 Batch: 3686/4218: 0.020581 0.027771 Batch: 3687/4218: 0.116337 0.130152 Batch: 3688/4218: 0.088838 0.095114 Batch: 3689/4218: 0.022345 0.028304 Batch: 3690/4218: 0.078824 0.087330 Batch: 3691/4218: 0.038548 0.045423 Batch: 3692/4218: 0.040589 0.047376 Batch: 3693/4218: 0.024262 0.031664 Batch: 3694/4218: 0.058842 0.065274 Batch: 3695/4218: 0.037341 0.042864 Batch: 3696/4218: 0.094297 0.099745 Batch: 3697/4218: 0.020922 0.026593 Batch: 3698/4218: 0.036923 0.047786 Batch: 3699/4218: 0.012094 0.018166 Batch: 3700/4218: 0.038264 0.043895 Batch: 3701/4218: 0.010264 0.015764 Batch: 3702/4218: 0.059450 0.064721 Batch: 3703/4218: 0.025389 0.030620 Batch: 3704/4218: 0.003920 0.009590 Batch: 3705/4218: 0.012285 0.017713 Batch: 3706/4218: 0.013843 0.018933 Batch: 3707/4218: 0.018481 0.023423 Batch: 3708/4218: 0.013893 0.019080 Batch: 3709/4218: 0.013687 0.018810 Batch: 3710/4218: 0.015351 0.020587 Batch: 3711/4218: 0.035274 0.040280 Batch: 3712/4218: 0.034682 0.040448 Batch: 3713/4218: 0.011583 0.016568 Batch: 3714/4218: 0.016185 0.021677 Batch: 3715/4218: 0.002972 0.008988 Batch: 3716/4218: 0.013648 0.018341 Batch: 3717/4218: 0.023556 0.028655 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0.097729 0.101680 Batch: 3774/4218: 0.024184 0.028301 Batch: 3775/4218: 0.020361 0.026263 Batch: 3776/4218: 0.019851 0.023669 Batch: 3777/4218: 0.046702 0.050595 Batch: 3778/4218: 0.021412 0.025467 Batch: 3779/4218: 0.012126 0.015757 Batch: 3780/4218: 0.019176 0.022861 Batch: 3781/4218: 0.019160 0.023347 Batch: 3782/4218: 0.021685 0.025682 Batch: 3783/4218: 0.052066 0.056127 Batch: 3784/4218: 0.005947 0.013046 Batch: 3785/4218: 0.019022 0.023159 Batch: 3786/4218: 0.111810 0.116451 Batch: 3787/4218: 0.065483 0.069132 Batch: 3788/4218: 0.027633 0.038244 Batch: 3789/4218: 0.027294 0.033399 Batch: 3790/4218: 0.058741 0.062231 Batch: 3791/4218: 0.020830 0.025200 Batch: 3792/4218: 0.012290 0.016036 Batch: 3793/4218: 0.041277 0.045123 Batch: 3794/4218: 0.146449 0.150155 Batch: 3795/4218: 0.007151 0.010908 Batch: 3796/4218: 0.016832 0.023535 Batch: 3797/4218: 0.021971 0.027076 Batch: 3798/4218: 0.016432 0.023673 Batch: 3799/4218: 0.006610 0.010270 Batch: 3800/4218: 0.023561 0.027057 Batch: 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Batch: 3829/4218: 0.135814 0.139295 Batch: 3830/4218: 0.063150 0.067663 Batch: 3831/4218: 0.027174 0.031764 Batch: 3832/4218: 0.029607 0.033029 Batch: 3833/4218: 0.016119 0.019964 Batch: 3834/4218: 0.023524 0.026844 Batch: 3835/4218: 0.020474 0.024844 Batch: 3836/4218: 0.012519 0.016032 Batch: 3837/4218: 0.076992 0.082139 Batch: 3838/4218: 0.038389 0.042221 Batch: 3839/4218: 0.016356 0.019463 Batch: 3840/4218: 0.011512 0.015027 Batch: 3841/4218: 0.068832 0.071930 Batch: 3842/4218: 0.038011 0.042054 Batch: 3843/4218: 0.014235 0.018408 Batch: 3844/4218: 0.016000 0.019971 Batch: 3845/4218: 0.023181 0.026204 Batch: 3846/4218: 0.011941 0.015423 Batch: 3847/4218: 0.013581 0.017139 Batch: 3848/4218: 0.011867 0.015988 Batch: 3849/4218: 0.029735 0.032995 Batch: 3850/4218: 0.052231 0.055191 Batch: 3851/4218: 0.014241 0.017268 Batch: 3852/4218: 0.085106 0.088207 Batch: 3853/4218: 0.008036 0.011022 Batch: 3854/4218: 0.006792 0.009733 Batch: 3855/4218: 0.039518 0.043679 Batch: 3856/4218: 0.020472 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Batch: 3940/4218: 0.010994 0.013389 Batch: 3941/4218: 0.020418 0.024346 Batch: 3942/4218: 0.040692 0.042989 Batch: 3943/4218: 0.022244 0.024609 Batch: 3944/4218: 0.006488 0.009420 Batch: 3945/4218: 0.010975 0.016418 Batch: 3946/4218: 0.018560 0.021191 Batch: 3947/4218: 0.101907 0.105347 Batch: 3948/4218: 0.038867 0.041346 Batch: 3949/4218: 0.006124 0.008445 Batch: 3950/4218: 0.036147 0.038396 Batch: 3951/4218: 0.011758 0.014305 Batch: 3952/4218: 0.016271 0.018931 Batch: 3953/4218: 0.018866 0.021164 Batch: 3954/4218: 0.027857 0.030379 Batch: 3955/4218: 0.033203 0.035722 Batch: 3956/4218: 0.034354 0.036680 Batch: 3957/4218: 0.008207 0.010918 Batch: 3958/4218: 0.025344 0.027747 Batch: 3959/4218: 0.043221 0.045756 Batch: 3960/4218: 0.066268 0.068685 Batch: 3961/4218: 0.017865 0.020262 Batch: 3962/4218: 0.066763 0.069219 Batch: 3963/4218: 0.090613 0.092939 Batch: 3964/4218: 0.018514 0.021321 Batch: 3965/4218: 0.022217 0.043258 Batch: 3966/4218: 0.015525 0.017813 Batch: 3967/4218: 0.033517 0.035941 Batch: 3968/4218: 0.009279 0.011546 Batch: 3969/4218: 0.058727 0.061182 Batch: 3970/4218: 0.013320 0.015569 Batch: 3971/4218: 0.012797 0.015211 Batch: 3972/4218: 0.014860 0.018663 Batch: 3973/4218: 0.009960 0.012854 Batch: 3974/4218: 0.016693 0.019294 Batch: 3975/4218: 0.020591 0.023765 Batch: 3976/4218: 0.025251 0.033099 Batch: 3977/4218: 0.014765 0.017498 Batch: 3978/4218: 0.114944 0.117474 Batch: 3979/4218: 0.016465 0.019035 Batch: 3980/4218: 0.027550 0.030406 Batch: 3981/4218: 0.023917 0.026168 Batch: 3982/4218: 0.025931 0.028929 Batch: 3983/4218: 0.043986 0.046370 Batch: 3984/4218: 0.017722 0.020387 Batch: 3985/4218: 0.031355 0.033834 Batch: 3986/4218: 0.022574 0.025031 Batch: 3987/4218: 0.023347 0.026641 Batch: 3988/4218: 0.051550 0.057467 Batch: 3989/4218: 0.022351 0.024793 Batch: 3990/4218: 0.014932 0.017650 Batch: 3991/4218: 0.034722 0.037578 Batch: 3992/4218: 0.055860 0.058081 Batch: 3993/4218: 0.008615 0.011427 Batch: 3994/4218: 0.009555 0.012084 Batch: 3995/4218: 0.041339 0.043493 Batch: 3996/4218: 0.017527 0.019703 Batch: 3997/4218: 0.036166 0.038680 Batch: 3998/4218: 0.138739 0.140911 Batch: 3999/4218: 0.077213 0.079386 Batch: 4000/4218: 0.154521 0.156884 Batch: 4001/4218: 0.031653 0.033897 Batch: 4002/4218: 0.018925 0.021313 Batch: 4003/4218: 0.018853 0.021042 Batch: 4004/4218: 0.159945 0.162108 Batch: 4005/4218: 0.025121 0.027833 Batch: 4006/4218: 0.032294 0.034479 Batch: 4007/4218: 0.025115 0.027975 Batch: 4008/4218: 0.059108 0.061878 Batch: 4009/4218: 0.053139 0.055271 Batch: 4010/4218: 0.054656 0.057364 Batch: 4011/4218: 0.027649 0.030772 Batch: 4012/4218: 0.039268 0.041518 Batch: 4013/4218: 0.013632 0.016526 Batch: 4014/4218: 0.014175 0.016466 Batch: 4015/4218: 0.040723 0.043466 Batch: 4016/4218: 0.079900 0.082333 Batch: 4017/4218: 0.047317 0.049744 Batch: 4018/4218: 0.044982 0.047437 Batch: 4019/4218: 0.081114 0.083402 Batch: 4020/4218: 0.078614 0.081535 Batch: 4021/4218: 0.040404 0.042479 Batch: 4022/4218: 0.038410 0.040507 Batch: 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Batch: 4051/4218: 0.020031 0.022081 Batch: 4052/4218: 0.036005 0.038894 Batch: 4053/4218: 0.016969 0.019362 Batch: 4054/4218: 0.017450 0.019403 Batch: 4055/4218: 0.082438 0.084471 Batch: 4056/4218: 0.044048 0.046093 Batch: 4057/4218: 0.016426 0.018870 Batch: 4058/4218: 0.014523 0.023197 Batch: 4059/4218: 0.010659 0.012677 Batch: 4060/4218: 0.053725 0.055737 Batch: 4061/4218: 0.040226 0.042307 Batch: 4062/4218: 0.016121 0.018661 Batch: 4063/4218: 0.075078 0.080368 Batch: 4064/4218: 0.016244 0.021906 Batch: 4065/4218: 0.015375 0.017579 Batch: 4066/4218: 0.025774 0.027781 Batch: 4067/4218: 0.019264 0.023267 Batch: 4068/4218: 0.007374 0.011936 Batch: 4069/4218: 0.019614 0.021588 Batch: 4070/4218: 0.039091 0.041340 Batch: 4071/4218: 0.032792 0.035646 Batch: 4072/4218: 0.003860 0.005898 Batch: 4073/4218: 0.007675 0.009580 Batch: 4074/4218: 0.007213 0.009187 Batch: 4075/4218: 0.020610 0.023970 Batch: 4076/4218: 0.030645 0.032864 Batch: 4077/4218: 0.024150 0.026156 Batch: 4078/4218: 0.028158 0.030376 Batch: 4079/4218: 0.010444 0.013107 Batch: 4080/4218: 0.009598 0.011710 Batch: 4081/4218: 0.085775 0.087944 Batch: 4082/4218: 0.009743 0.011930 Batch: 4083/4218: 0.014053 0.016095 Batch: 4084/4218: 0.022451 0.024582 Batch: 4085/4218: 0.034356 0.038981 Batch: 4086/4218: 0.009302 0.012321 Batch: 4087/4218: 0.003924 0.006515 Batch: 4088/4218: 0.008610 0.010619 Batch: 4089/4218: 0.019477 0.021404 Batch: 4090/4218: 0.019922 0.024993 Batch: 4091/4218: 0.006696 0.009046 Batch: 4092/4218: 0.020640 0.022951 Batch: 4093/4218: 0.042850 0.046503 Batch: 4094/4218: 0.007931 0.010457 Batch: 4095/4218: 0.024693 0.026617 Batch: 4096/4218: 0.023259 0.025158 Batch: 4097/4218: 0.039286 0.041227 Batch: 4098/4218: 0.022902 0.025145 Batch: 4099/4218: 0.014401 0.016578 Batch: 4100/4218: 0.008029 0.010083 Batch: 4101/4218: 0.019357 0.021204 Batch: 4102/4218: 0.031069 0.032895 Batch: 4103/4218: 0.056056 0.058207 Batch: 4104/4218: 0.012876 0.015019 Batch: 4105/4218: 0.024375 0.026752 Batch: 4106/4218: 0.011602 0.013628 Batch: 4107/4218: 0.038549 0.040606 Batch: 4108/4218: 0.017987 0.020004 Batch: 4109/4218: 0.032537 0.034710 Batch: 4110/4218: 0.030424 0.032387 Batch: 4111/4218: 0.023844 0.025736 Batch: 4112/4218: 0.012634 0.014396 Batch: 4113/4218: 0.031143 0.033168 Batch: 4114/4218: 0.011175 0.013086 Batch: 4115/4218: 0.011740 0.014974 Batch: 4116/4218: 0.056011 0.058384 Batch: 4117/4218: 0.017453 0.020240 Batch: 4118/4218: 0.057085 0.058889 Batch: 4119/4218: 0.016952 0.019935 Batch: 4120/4218: 0.031320 0.035830 Batch: 4121/4218: 0.038744 0.043683 Batch: 4122/4218: 0.023911 0.025864 Batch: 4123/4218: 0.017109 0.018986 Batch: 4124/4218: 0.012546 0.014336 Batch: 4125/4218: 0.009073 0.011418 Batch: 4126/4218: 0.029650 0.031755 Batch: 4127/4218: 0.030447 0.032628 Batch: 4128/4218: 0.120122 0.123246 Batch: 4129/4218: 0.024213 0.026097 Batch: 4130/4218: 0.017300 0.027139 Batch: 4131/4218: 0.038641 0.041905 Batch: 4132/4218: 0.027826 0.030601 Batch: 4133/4218: 0.104024 0.107234 Batch: 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Batch: 4162/4218: 0.161439 0.163674 Batch: 4163/4218: 0.026101 0.027987 Batch: 4164/4218: 0.055276 0.057016 Batch: 4165/4218: 0.035224 0.038508 Batch: 4166/4218: 0.029048 0.030800 Batch: 4167/4218: 0.024186 0.026019 Batch: 4168/4218: 0.022643 0.024587 Batch: 4169/4218: 0.026938 0.031445 Batch: 4170/4218: 0.038199 0.040249 Batch: 4171/4218: 0.019376 0.042827 Batch: 4172/4218: 0.019940 0.021698 Batch: 4173/4218: 0.073754 0.075686 Batch: 4174/4218: 0.015477 0.017807 Batch: 4175/4218: 0.012037 0.013820 Batch: 4176/4218: 0.010983 0.012814 Batch: 4177/4218: 0.017085 0.018920 Batch: 4178/4218: 0.024900 0.028617 Batch: 4179/4218: 0.101419 0.103353 Batch: 4180/4218: 0.033497 0.036246 Batch: 4181/4218: 0.013204 0.016824 Batch: 4182/4218: 0.011522 0.013531 Batch: 4183/4218: 0.022753 0.025344 Batch: 4184/4218: 0.013518 0.015256 Batch: 4185/4218: 0.050149 0.052083 Batch: 4186/4218: 0.024543 0.028193 Batch: 4187/4218: 0.047666 0.049611 Batch: 4188/4218: 0.020604 0.023571 Batch: 4189/4218: 0.012124 0.014582 Batch: 4190/4218: 0.046906 0.048608 Batch: 4191/4218: 0.019727 0.022605 Batch: 4192/4218: 0.023547 0.025440 Batch: 4193/4218: 0.020740 0.023053 Batch: 4194/4218: 0.024749 0.026631 Batch: 4195/4218: 0.035272 0.037125 Batch: 4196/4218: 0.033635 0.035274 Batch: 4197/4218: 0.008602 0.011729 Batch: 4198/4218: 0.029421 0.031225 Batch: 4199/4218: 0.036646 0.038773 Batch: 4200/4218: 0.010344 0.012531 Batch: 4201/4218: 0.027773 0.030510 Batch: 4202/4218: 0.045037 0.047138 Batch: 4203/4218: 0.010714 0.012516 Batch: 4204/4218: 0.012176 0.014356 Batch: 4205/4218: 0.025144 0.026920 Batch: 4206/4218: 0.038580 0.041043 Batch: 4207/4218: 0.011275 0.012884 Batch: 4208/4218: 0.012204 0.013871 Batch: 4209/4218: 0.030312 0.033369 Batch: 4210/4218: 0.037559 0.039477 Batch: 4211/4218: 0.027811 0.040460 Batch: 4212/4218: 0.015112 0.017266 Batch: 4213/4218: 0.018557 0.020630 Batch: 4214/4218: 0.059925 0.061729 Batch: 4215/4218: 0.019206 0.021092 Batch: 4216/4218: 0.037423 0.039287 Batch: 4217/4218: 0.038459 0.040419 Batch: 4218/4218: 0.034828 0.036852
plt.figure(figsize=(12, 4))
plt.plot(aloss)
plt.plot(sloss)
[<matplotlib.lines.Line2D at 0x7ff1ef5b76d8>]
plt.figure(figsize=(12, 4))
plt.plot(aloss[-500:])
plt.plot(sloss[-500:])
[<matplotlib.lines.Line2D at 0x7ff1ef2edd30>]
4. Results¶
We plot an image to see the results
def revert_to_rgb(img):
# Revert to RGB
return np.clip((img + 0.5) * 255, 0, 255).astype('uint8')
from skimage.color import *
def revert_img_special(img):
# Transform color image to different representation
img = hsv2rgb(img + 0.5)
img = img - np.percentile(img, 1)
img = img / np.percentile(img, 99)
return np.clip(img*255, 0,255).astype('uint8')
#return np.clip(((hed2rgb(img)-0.5)+1)*255, 0,255).astype('uint8')
# Select a few images
n_examples = 20
selecter = np.random.choice(np.arange(len(fx_train)), n_examples)
x_valid = np.array([load_img(fx_train[s]) for s in selecter])
y_valid = np.array([load_img(fy_train[s]) for s in selecter])
#y_valid = np.array([load_img_special(fy_train[s]) for s in selecter])
y_hat = np.array([revert_to_rgb(i) for i in tqdm(generator(x_valid).numpy())])
#y_hat = np.array([revert_img_special(i) for i in tqdm(generator(x_valid).numpy())])
# Let's plot the prediction of one example
idx = 1
plt.imshow(y_hat[idx])
<matplotlib.image.AxesImage at 0x7ff1ef11f1d0>